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   "source": [
    "# In Depth: Naive Bayes Classification"
   ]
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    "The previous four chapters have given a general overview of the concepts of machine learning.\n",
    "In this chapter and the ones that follow, we will be taking a\n",
    "closer look first at four algorithms for  supervised learning,\n",
    "and then at four algorithms for unsupervised learning.\n",
    "We start here with our first supervised method, naive Bayes classification.\n",
    "\n",
    "Naive Bayes models are a group of extremely fast and simple classification algorithms that are often suitable for very high-dimensional datasets.\n",
    "Because they are so fast and have so few tunable parameters, they end up being useful as a quick-and-dirty baseline for a classification problem.\n",
    "This chapter will provide an intuitive explanation of how naive Bayes classifiers work, followed by a few examples of them in action on some datasets."
   ]
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    "## Bayesian Classification\n",
    "\n",
    "Naive Bayes classifiers are built on Bayesian classification methods.\n",
    "These rely on Bayes's theorem, which is an equation describing the relationship of conditional probabilities of statistical quantities.\n",
    "In Bayesian classification, we're interested in finding the probability of a label $L$ given some observed features, which we can write as $P(L~|~{\\rm features})$.\n",
    "Bayes's theorem tells us how to express this in terms of quantities we can compute more directly:\n",
    "\n",
    "$$\n",
    "P(L~|~{\\rm features}) = \\frac{P({\\rm features}~|~L)P(L)}{P({\\rm features})}\n",
    "$$\n",
    "\n",
    "If we are trying to decide between two labels—let's call them $L_1$ and $L_2$—then one way to make this decision is to compute the ratio of the posterior probabilities for each label:\n",
    "\n",
    "$$\n",
    "\\frac{P(L_1~|~{\\rm features})}{P(L_2~|~{\\rm features})} = \\frac{P({\\rm features}~|~L_1)}{P({\\rm features}~|~L_2)}\\frac{P(L_1)}{P(L_2)}\n",
    "$$\n",
    "\n",
    "All we need now is some model by which we can compute $P({\\rm features}~|~L_i)$ for each label.\n",
    "Such a model is called a *generative model* because it specifies the hypothetical random process that generates the data.\n",
    "Specifying this generative model for each label is the main piece of the training of such a Bayesian classifier.\n",
    "The general version of such a training step is a very difficult task, but we can make it simpler through the use of some simplifying assumptions about the form of this model.\n",
    "\n",
    "This is where the \"naive\" in \"naive Bayes\" comes in: if we make very naive assumptions about the generative model for each label, we can find a rough approximation of the generative model for each class, and then proceed with the Bayesian classification.\n",
    "Different types of naive Bayes classifiers rest on different naive assumptions about the data, and we will examine a few of these in the following sections.\n",
    "\n",
    "We begin with the standard imports:"
   ]
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   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('seaborn-whitegrid')"
   ]
  },
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   "source": [
    "## Gaussian Naive Bayes\n",
    "\n",
    "Perhaps the easiest naive Bayes classifier to understand is Gaussian naive Bayes.\n",
    "With this classifier, the assumption is that *data from each label is drawn from a simple Gaussian distribution*.\n",
    "Imagine that we have the following data, shown in Figure 41-1:"
   ]
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "X, y = make_blobs(100, 2, centers=2, random_state=2, cluster_std=1.5)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The simplest Gaussian model is to assume that the data is described by a Gaussian distribution with no covariance between dimensions.\n",
    "This model can be fit by computing the mean and standard deviation of the points within each label, which is all we need to define such a distribution.\n",
    "The result of this naive Gaussian assumption is shown in the following figure:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "![(run code in Appendix to generate image)](images/05.05-gaussian-NB.png)\n",
    "[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Gaussian-Naive-Bayes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The ellipses here represent the Gaussian generative model for each label, with larger probability toward the center of the ellipses.\n",
    "With this generative model in place for each class, we have a simple recipe to compute the likelihood $P({\\rm features}~|~L_1)$ for any data point, and thus we can quickly compute the posterior ratio and determine which label is the most probable for a given point.\n",
    "\n",
    "This procedure is implemented in Scikit-Learn's `sklearn.naive_bayes.GaussianNB` estimator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "model = GaussianNB()\n",
    "model.fit(X, y);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's generate some new data and predict the label:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(0)\n",
    "Xnew = [-6, -14] + [14, 18] * rng.rand(2000, 2)\n",
    "ynew = model.predict(Xnew)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now we can plot this new data to get an idea of where the decision boundary is (see the following figure):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kSeWC0WlW2TsakRsaQRjzkaf28f0A2zKxDE1U1JnYsldsnes7q9y7s1Zsp0Uy0VQFxwsIowRDVzF1DRmQVUUMbSceVVtsn8M4pVFdbA+oqsLVjS5eEAKi5TK73sOxw9FgiqGr+EGI4wdcLdo9p89xEMYEUVwmpmURJymSJPOBj97km37g3zOausiSRJQkfOGnfxzf8nWfR5yEWPritdQ1lXrFZL83KnZsMp1WlUV9uBxZkthea+H4PkeDKUhikbJNHT+IME2NNM0W2hAgFu3B2GE48bh9OGRtcxvPD/HDiJ31NmmacfOgT5blSBL0hg5XN8UQsVGzCaKYiRsU91LlTAvGtgyuba2SZqKtoyjifwe9MTlitrPaFtWnoWvcs9klSTLqNavsS89i/tzalsH1ndWF87DarvHIRzyevHlIkmZsrTaRJAll7u8kWSoT7fFwUpy9xbhsAs+yDL+ohr2iOk3SjMPjEde2VsgRhWEQiddomobjh1QsHUPTcL0IVZFI0qzYPcjlzvt0WKbOWqcuCg5JomYZmIb6nPrrs4jipGizyEsrdTEIDdhea9MbTRmMnQL4YYvWlxfiBdG57/+CJPHZlkdTxVYsiGKqz3MSnw/T0Fhp1YiTFE1dHLyFcVJW3ACqLC1UklXb5L4ra4ROn52N5b1wL4hwvKCs8OMk5Xg45epml25TLBjzi1SW5eTk5XGUW8MlgpKqqrK12ipQBovtnstEvSr6qGPHxw9Cek7IerGVH099Vro1XvfAA6RpimlotBpVdE1lflRwPJgwGLsoijg3k6lAkswWoN2jIaOph6oIRMDpXiWIRfV04gEYTr3yftA1lakXEkRxOTcR5ytj72iI60fs9ya0D8SAeVllpMgytw8HfM13/jTBqW3w237/YdI0559+21eiqyr23GekacbUC7h9NCCKUqoVk1c9cIWx4yHLMmmWU7UNLFPHCyJswySvSViGhqlrjB2fJ24cUKuadJt1rszdK6K9I/rrE88vBlo5laL6DaOEIIzK34FotQ3HLusrTWRZZmOlxUpLJJ7zCp5Z4p5FrWKVs6XT50rXNVoNm7EToKsKcZrSrJ6P5uiPxAIk3tdAlqRy15TnoMjg+BG2oRGnKbIkBpO7R0NkSaZWEQP7o8EU2zIuBQ6YXZfdwyF+FBFGCUf9MfdfXSdJUrI859mDHqqsIMsSU8enWbXRVYW6bdJqVIo8I5K2UrTNojihswTBNPu87fU2h/2xOBe5KIQuQildFBPHY/94BJIM5Ky162eeD0mS0BQFU9fQNBUviNFUhbWO6IdLwEVisy9IEs/ynNmtIrbsy5PSbDglz/Wln0sMxg7Hg4kYpGYZnWatrNIrps6B65eJPEnTBfgdcKbKWHac88enyBJRcv4Dp6oKlSIZmLpKGCXiAp7zAEnS5VA558VsUPY+WaZiqei6ilxALz0vKpAOZ98/TTOSJGU49ajaoieraxq3Dods5k3k4tiatQpXNtromnpHqOGZ7wbkLFa7p2M89XCDiFrFpGIZxEnKcOKW13A+LFPnl3/nT4jjs0igOM15+x88zPd+0xu4dn1lsVJ2XG4dDlhrN9E1UVzcPOjz0PVtklRspe3imvVHDp1mha5U5clbR+R5zs56m5V2jbHjsdZtLNxDEzdA1zQkCZI4IUpTpl4gznl+0veev8UlSSI7dS4kSSyiSZpdejGXJIGuWBaiZ+0ThDGWqZWLrB9EDCYueTFMTZKU/tgp5wW7h0PSNKdTFChxkpJnGdWKxdT10TWBkFIUmTCOqRYtSEkSUOFZe/IyMXE8giimZpvIkmhRTd2Aqm2gKjL7R2MeuGeNJM0EtFSSWOs2WO3U2T0aMvUCVFXlJVfXCaIUSGnXq9SrFnunPsv1AnaPRsRJwvHIoWpqbK627whpPi/SNOOgNymQJyL3HA2m5XkOi52DaWisd5vcOhiABK26RZSkaJpaDpgvev5fkCQehBFRLJAptqFjLUkgfhCx3xuRpKKvO8NqGnMQuctEmmYcD51yu5/nOYOJW2JIGzW7SApe0bOrU7WX973PC0NTy6GNgI9FrLQuHlxtrLQ4HkzwwgjT1Flt1e6YAMUN7BMnGZahLVSSlz3OTBHQQVVSGLsBL+k2y17r/Occ9SeMHJ8kSZi6AZX1NiB63rah43gCwhnFYtikFwiANM3u6vp0WzX2jkaoSkqa5Vj62ftB3AMnC5wiy0QXwDX/5JGnSbPllYth6NzY6/PKB64s/D4IxPvpmvgco5gRqKpc9s1BVMiqctIqkfKcOBULtqlrJKaxgMYA0BQFJ/Y5HvhEccre0ZhbB31x/1WtIiFDb+QQRDESIlmvzQ0/ozjh9uGAJM3I85x6xWS927xUIp9Vn2IRPrnWkiTRqNk05tbCIIy5edBHV8Ux7R4OxXOnaSdDUl0tWxcAaZahayrdVu3MwlqxTLwgxDaNMnmfV6yU75dmOF5AlmW4flRCPVVFQZaV4p6roGsqY9dn73BEtWpytWgRZVmOaRQto/SkvTT7/POw3vu9MZoqMxgHaLKE40UMxy6aqrCypI16p8iyDKSTHbcsyyCJFtON/T5BFKEVu9fN1RbXtrpEScr2aoswihk7Pqois7HSvPCcvSBJ/OrmCn4QoigyVds8k7zSVOBeNVVFVxVu7Pd55vYxq906aZKVQ59m3V66RZ8PgcM8qZTFf/NyeyJJEivtennzPZeKf4ZVPh5OSZKU1Xad1pKWwnwoisz6SvPSn5HnOQe9EWMnQFUkemkmPuecbeGyWO/UCWWL44FDFCVcXW/z8Q9eOfOdx1OPkeNRs02yXKM3djjsT+g0q4RRwtWNDrqm4oURlqWz0qwJPHB/QpZD1TZY7yyH2J2Oqm1yZaOD64coslS2f0YTFzcQN7muKcSJWCzyPCeM43LwJHqu4rw3qhbNeoX2BZVTlmakWX5m91SrmmRZViIg/CCiUbPPIIo0VSZOM6zyZ5W0SKxpJmrn01vvVt3m1kGf0dSlVrW5utGi06hi6RqbK82y0rq60WE4ccnynLVOfWGRPh5OIYdq8buxI2CzpqGhnhpez0eSpOwdjQjiWFTWVZvVzvmwNccLUGW5XNizPCcIYjRNwSjSha6pVEydiRsgS8UztCqeH5F4BSrMMg3W2nUOeiOmnri+myvNcnAdxQmHvTFhLIaus5797cNBOZPxgghyCoiqTKMmuAwH/TFhlHD/9iq7xyMGU48wSri22S0XYlmW0ef79BIF/vtsIs8ycf1kSaB7KpaB60fYls5oKvDpd5sb1GLxCwpQQxDG6KrKjb0etw4H2KZBngtkXK1i0ajZResuK1ubsxiMnfM/566O6nkK09DOhRTBjMUmLtzU9cmynCBO2T8ccftoSLtR5aH7ttg9HLK1xoWJfL51YWgqYZxg6fqZNsdlL1AYxUuxoIausb3WXnhtEMYc9sdicGqbrJ6DHb5TxHHCxPU56k/ptqtISIJRN3JoFjjgy4Shq7z03quMJi4ArUZl6TbND+Ny5Zclme3VFlGUoMgy7UaFVr2y8D2CMGb/eIxlaoCE6wX0ZFngys+JNM046I8FxE0VPd/Z+ewNp/RGU0xdw/MFcqbbrDKYeARhQrdZo1axFrDfiiJxOJiQA3/7yz+T9z7yNK4fnvlcXVdZb1dLdqKqyGiaSq1i8fL7dnj06V0mbka7YXP/EoZerWLR8EPGU5/h1CWMIjRFY/94RLtZXUoG0zSV9W4DQ1cxdI3Ea7HWbWAbOlmW44chEhRIjubS8xXHgnmbJCmyIhOEMU/fPsK2ROW/tdpe+kwNxm7R0hDJfzhxqRZtqWUhFwPJMnJo1myyPBOIG3JqFYt7t7psr7XIC2SI6I2LAb/rR8gSZPmUrdUW2+sd0jRbaIumacatgz6SJOYKrh9x0B9Tr5hl+wRE9R2EEXHBH7m63sENInYPh4K5jESnWSNKxOAvY9aCXfxeJ4zWDAmxmMyHosjifgtC8pyyhy5JMpJ0eRTNfEiS+JzD/hjHD7ANnVa9wvsefZZG1ULXRAEwnLjESVqSH7NcMDe3VtsYBab+xl7v3M95QXHi8zFjLgnihVz+Ls0ywihi4ristOo0qhY5OaOJR7dVZeIGFyZxSZLYWGnRG07xw4iqZVyaDXg6Jo7HfgFdyvOclXatxHmejiRJuXXQR1MVKqaAnIEgW9xNBGHMzWLrtd8bkZOx0q4XPbazFeWdwjS0O+4ABKTNx9Q1slygT9r1Cp1m9cy8ACBOxGCuP5qSIfrcWZYvTeKz4z3oj/H8kKotety3Dvpc2xJkn+FEsBVn2/+pF7DartNu1ojdQblrmnoBg7FL1TKo2AYV02A09XjDZ72aX3jbe3jHH39kIZFrqswP/G9fzl5/TBSnrK+2yHPYWm0WDN4ma536whb8dMzuJ8EAjQuEi0QYx6x1Gucmx0bNxvECTF0jp0jKFZNndo/JgTzLqVbMsjI/Hbqq8Niz+2iaSppkjByPlxfMZ8HoHHJta+XM3wZhtNAuUxSZ6NTgeD5qtslo6uEU6Iw8z+m0Wuhlf1ZU4pP+wZm2ox9EuH6IrqqMpx5RHBNFCa94YOfMuYyTlDTNyvcQDO4Q29CY/wayLFGxDK4U9Pr94yFJmlKvWmR5Lu6b7RXAxjYNFFkWKJS5Ik0sLkMBLzV00jRj73i0wF0Q17XJ3tEQgOHUYbXVwPNDttfv7pmdD01T2V4/GXIHYSzygR+KXZ5EiVzZPx6VjOooTtg9GmCbQsZg4vpUz3nM/z+RxAdjh95QbBcaVYvVTp2NlQZ7x2OSJMfzY9r1KqoiE0QxGysNwlhM+GVJYuoKuFeWZbSKZLMwaFQurgovE1mWC1igeQILPB5OqVespcPLKE4WttYzJML6XSbdw/4YXVOwzQpBEHPUn2JoGnLRS7vbQeJlolGz8cKYqRtw2BeQu6RicGOvx+Zqc4HlGoQxB8cjPvLUHjsbLSqWWeDbh1zd7JZJP4xi9o9HRHGKZag4XljizzVVISz0W6wCXrmwOOXic7I8J81ysixj4vg8dfOIkeOSpkIHZaUt6N+yLPNLP/xN/Mo738e//E+/y62DPg9d3+Kvv+HTWW3XeeyZPTa6TaqW6NPuH4+4vrOKLMtntuDnRZKkRHHK7vEICUjTnFatcm5yrNqih90fO8Rxynq3ycTxS5o4CE0Zr2afeY88z4vq1BKJMc0gP+nf65qK44cFCmXx3rItQyx0tlIABbILh2SapnJlvcPxcMJhb4ymaQzGDqvtxoW7ZxAj6TAUGiqjsUuaZ8SpoNKfLhxkWRKLV3GdZ5W6bRlIxWxAzJnihd2JH8YYmsCjj6ZeycysWJZgMmd5iUJxvACv2BXEcVIWe4oik+ecGbDqmso9WytUbYNb+wPRJiP/mEAFp0PXFJp1W7SK/IgoSbm62cEydYHyKRY7XVPpjx3CMKFZt9EUhdHxcmz785rE4zjmu77ru9jd3SWKIt70pjfx2Z/92Rf+jeMFHBXaCpIkMZx66JpCq1HluqGTpBmr7RoffOI2eS5YTkmSoyqiSrdNnb2jEbapI8sqvZGDLEtzTKiT7Ypt6tSr1nOqwrNcYCjmYYEzWvGykGWZfG64FicpmnJ5oaT5vzMLNMn6SoMkExohq63qUijfLC4a4sziNDV9/tg3V5pMLL+kOc8etOOhUybxIIy5sd8jz3J0XeWZ28c0KjZuGFKv2Tyze0y3KajUtw8HyJJM1TYE+mHkYlk6uiq24XlxHJIksdKqcdAbo6kKcZrh+QGH/RxJlnl2r4+bPMXNwyGO51OzLHRFJc0y+mOHV73kavkd/uKnPMRrH7pGf+wwGDmClRqE1G2TxvwDDUu34BdFUjD7ZpyD3tBh6voX7nLEENEmnPZp1GwGY2eh5y5LUrkjnY8sy4nTnK2ifZFmOY8+tYcfRkViTwpS19l7q92oEMUJjh9CLgb3OfDM7SOyolXSbiy25MQAPaRVgAkcLyTLRqwVc45l93AUJwzHLk/eOqQ3cNhYqRNnGTVJYfdoSPsUTE/XVDqNqqCdF0l3a7WFoWtc2ejQHztkaU57pbJQNIjWixiUbq+1CmkKaFRNkkxA+DRNpTec0h85aJoYhA7GDrquQi4xmgqcubrk2Q2jmP7YLecGYZSwfzwqBco+1pBlWYiIaWpJwJrNAkTPXrRqBa/ARZElbEvHMnVG57zn85rEf+M3foNms8kP/dAPMRqNeMMb3nDHJB6E8UJyM3UVL4xpQamZsr7SxLZ0DnoT0lTgQ9c7DepVC8cLkGWpTES2qeG4Ae1GVQgDHQ4J41icGMcjTtKl0LQ7hSJL2EUSsAyd0cQr6Ocsrf5NQ6NVrzCculCQ1HfW2+e9/blRr1gMpx4VUyPLcrrNOte2uoJVOVPlU5USfSOIRGOmXlioPlaXtnxOU9PXu82FHcVMomCmOwGU9PRZjKcemqKgGgr1ioXvh+weDWnUbAxNo1GzGE5cDF3F86OCDi4SR61mEQQxkSIo4p1mtax4mnWBPPDDmCiOybOMetFGOxpMcVOdbquKoSm4foisSLQbNTqtk3aP6C+KPn2zVmE88bBNA8tQURUZXROfFUQxhqbe9ayiZptChsELgZxm3bprLHGtYnE0ELs7JKFKuKzqk2UJXRVbbFVV6A2nhHHEs7f7tBs2nVatFKk6+7cym6st0jRDKrbuz+4eY5k6miRxPJwgSywM0aIkBekERZLnYtFwgwhdVc6wB5Mk5eZ+D1mSader3Dro4wcJq90aqiIYlEEYMymIbzOBuW6rRtU2SdIUXVNPhK90jc1z2o4rrTpJMirZv6966RXqVYEwm0lT5Hle7D4ELNYsNE2GI5fj4QRN0+g0Kxz2HJIkXbjvxXlalNqYesFdty0vCv1Ui2UWmytN9nsjhlOX44HD1kqD3tDliZuH3Lt9/iLyvCbxz//8z+fzPu/zAHHhFeViKBGI7UVyqmKt2mbJ0JvR8etVm4plLkh/ip55ThCKKbKERJJkmKZ4EMIowY+ickiiawrDiXsm4V4mJElivdvkaDhhNHHpTxwh96mLbc989T+L1U6detUSEKwiId5tdFviPScFlv3KRgdNUwsSwRhFkUhSoby4uSLU7yZuQK04h8eDCUaBQfeCCD+IUBRBpbYNQbJx/ZCj4eTMg2PoGrqqCEiiohBEcUlemp2TPBc4fiSQVRlZUQqxMMjSHEkW4ku7x0MahebFzf0+nWaVqxsd0ixD09Qzycu2DGzLwPECpm4oyC9Tl8HUR68kpUaJVJCzTFOnM4eVm7pCl0NVFNJUwAfHrkfNbvGql1yhP3ZxPEGX31hp3PX9YJk6q+16odqoEMYxlbuEpiqyxNQNuLnXI80y7tnsMnF82o3KQptsJpB0+3DA0dGU0dTjpdc2sQxBMlpt1c6cPz+IGE09ZEmiWbfLfw/CALmQEcjJUWSZ46FTIiPEcYld5Kzq3z0aYVsGVcsgKNpi8+EFIf2RU6ggSnSbNZGYVY0wjmnWbPaOhqiqeP/B2C3ZqKJFc/l2haoqbK+3z6hWzi/Cy4gxhqZh6irVilVKxu7t3sb1wwVZiXmpDUWR8YOIiqk/bwn8oqjYJvdur3Jrv0/VsrAMjYptsns0vFCzScovogI9x3Achze96U18xVd8BV/8xV+88G/ve9/7sO2TkyZ6yw6uHxWDLIXVdpXhxMfxQ+RiVVzv1BYqnSTNOBxMiOKU3lBsO1oNG1WRWe/U0VSRdPZ7UyqWqM6yLCeMEq6snx0eZVnO2PUJQ6FLUa9YC9vTIAgwzYKa7wb0J14pEZmmorWz2qoW1F75OTO8Lhs3D4ZomlIOgR0vpGJq3D4eocgK3UYFXRfVrKmp+FGM5/poxYMjWlEzdmCE44Xcs9kRVPy5cyN0t33SVLSuapUTIaYoTtjvTUjTjMP+FE1XUCWJNBO0607ThlwMrY6HTkmdDuMEU1PKRa/brFC1l/eS4yTlmb0+w6mPoao8fuOAKM54+X2bZHnO4WDKlbUm13e6mHOJrD92y8pxvzchSlI69Qq6rqAXSoIzItlzjakbMJj4ZHlO1dLpNCoL75fnuRBRk6QFWdsgCEBW2D8WC+zxaIrrR7QaNqamiuF7a5kmukioyCIpgaCh12yTVt0uP9sPYw76EzRVEb3fLGNrpVEu5EeDKaahlwu+qipsduusdWrl/TSYuIynAVGS0Bu5XC3IXCBgf6sNC7tQrtw9GvHMbp9mwyZNc8YTjzTPadVs2nUL09Tww6TE/4dRgm1qdC7Q1v9YYzj1GE19NFXolJuGSp7lZJwk6uFoyuZqq5TcmIXnh/TGHlmeo6sKq+3aubLE/zNCPCsRUZwSJ6Ivv9ltoCv5n48pxP7+Pt/0Td/E13zN15xJ4LM4LcQ+IyPMaOV+EHHrYMB9xckNowRVkRdo7wfHI6x6p8BaiiHj5qrA387rZa8cDkq8cZwkS7HVM+q4VonQNTFkq9rmggrdvIC8MEgYUi+OL4hiwmIQIxVVzEzo5/mIPM/xgogkEdtO09BQ7UPsuQrh1n4f3dBodNYKkoDClfU2XijoyrUk5dGPPsHq1jpBELHeqrParpXfZaUmU202adbsuxoCPxgnTByfm3s9KraJbRkc9IaMHZ9rm102V9uFvKdbVMUZ+8dDVFXhyka3JHRc3eyeOzirt4+FdrWqomkKmlUnjFJWmjavfuVD3H917cxwOY4Tbh708YOYCIHB3VxtlkJW9+2sPi+CRnmeF7uR07hjQR5xPFFBzeOzH330UTa3r1JtTsRg82iAoWvkWc72epupG3D/1bWlQ+uV9QljR5hZHPSG3O7voZgmXavFvTsr6JrK3vGQZjcuFzUvCGnVq3Sa1VLCYL83JpJMNjaFUFucpLTqlYVWYxDGhFHE7vGIesHnmJlMBNN++Two1j711gpRnCLJEi0v4NUvu8ZK8V7HgwkTxy9bXQI2qrG+0igXhjzPSeZYzh9r5Tsjxrl+JDglNRs/FNBETVPIs5wgfIqP/7hXLN0hnzcv+vOIq37In37oGeQ4QQojvDCmu7bJpLe79PXPaxLv9Xp8/dd/Pd/zPd/Dp3zKp1z6707TStMsW7iImiqXNPZZRHGCpqrl31dtA+2UdsRsGzqeekJvuVAXPB1Jki6YSMxgbTOnkThJ8cOYo/5EbN0Nlaqll1rU5IIlVquaJXLlaOhQOwe5crdx1Bd64JIkqrGNFZFs+2MXyxDORH4Us16oLyZpxnDs0Z+4bHQa7PdGuEEovr9lEAQRsiTheAE39/tULIPNVQEjGznewvb7TjFj6tWrFntHQ/wgotus8coHrpTvoSgyo6lbiFNBmKQl1FKWxaAsSVPO21aLIZYwxIj9EZvbm1iazkq7hq6LWcFw7JBmArM8E5+6st5h6opqstuoljKtiix9TBX4fMz3T+djOPFwvZN7ajh1sS29REioqlI6x8wgh7ZlFOJe58tMtBsVglDI4j7yxC02Ok0UVeLRZ3YJooiPe8mVUiZ4FjM1ShDne2utTZblWIZOvWqK1krOGRbsjM8hSRIHvQlIogW0vdbm6WmfJEkJ44Q0y+m2a8RxSpplQst+DmFTr4q5ThDGJFnK7uGQbrOGH0W0ahU6zQpHg0kp8FWvmJdyi5qPuJAjmGnwSJJ0xi+gapvsrLcZOz6KLLPRrZ/b4rxIruBOMSu60jQri667CVmWadZtDocTnCCkaho8+vQeW+eQRp/XJP7TP/3TTCYT3vKWt/CWt7wFgJ/5mZ8p2xCzSNOMJD0rRjULQ9egmNQqsowbiIs9HxXbpDdy0FRF9MbTHNM4i2GWZZlKQQ9XigR7p1V+9powinnq9hFBGPNnH7lJplZZadfpjz0aFZMr6x0BzJdlbh4OTiFXlgtanRdhJCCT2ikDjChOGE29cssn9BcmXN9ZRVVkQYm3DK6sd8iZyZI2UWSJjW6D1XadwcSlP5qWOx5dE6YOnUJlsdOYQw7k8FwabDN4Vlo4orzrTx7jo88esL3W5gs+7RVc3egyLapSRVFK5M6skr1Im6ZqGxwNBGnKD4UN22q3UZIlbh8OCMKIwdjh6b0+FV1nc63JeqfJ1lqLa1KXo+EUCkr79trZIeDzObgCsTubQQBBUO/DKCnlVG1TED9GUw9VlvGCiFajgutHF/boRU+4xcTxuLrRRVYkJlOfKBbkn/VOg1a9wvQgwA8icoQqYm2ueJEkiXazihdG5TMRxjGt+nJ1zHpVYLBnJJQkzRhNPR57Zq/Q94gYjGO6LdHL77YqZ4hwV9Y7jCYuR4MJq+1G+VnDiUsUx7jBCcFn4oZYhn8h+mo+HC9gr+jT53nORrdxrtnLbNYC0D+6XIF1t/fGfNGV57Cxcv7xnBdZnjMau1i6RprneH4I9eUtx+c1ib/5zW/mzW9+8x1f99Sto3JVX8Y00zWV7dUWh/0xQRTTqlXKAd8sWnWbMIqZFtXw+inRoVl4fsjto6EYduQ5zVrlDO1Y01SaVZvh1EVTFJI0o92o8Ngz+0J3WJIZTz2O+hM6zZpwu3EDVjv1svI2NU1IkRqaUEZU5Ev30Q57QktDkmVsQ+OerZXyuwhp0pNjFZ9XnIPGiZ/kjBQUxSnDiUOcZIynfqHjUiuYe0n5AMuyLHrFccJTt47YWGlgmwaGri4kn7uNZ3aP+YJv/GEGY5e4WKg1VeG//sT/wWtffi8gEspMnAiEfsqyawdicbt1MCCKU6Fxni16X/phRBDFJGnO2A3QVRlVE3yC0dTDNDRWCwr7rPKdr76SJBXEoyDE0LSCWfmx44JNXcPxxHA1L/RV5kk3kiSEmlqNClc3O8SJ8No8GfadH7PCJEcoUVYsgzgVwk5j16ddDI2nriDs1CvmmYpTuA/V6Y0c8hy6zdrCgA9EwTB1g9Jr1TL18nl64laPrcyk26qy2m4wGDk0qxWqFROz8MtU5uQAZiSzKE6YbRRycrJC0MqY+86aKi+YXlwUWSacuqw5b9CD3kSYVnyMu+AsyzjqT5h4PoqssFHkGNcPieMEXdfOYPrDKBaSFXNF12F/smBkcadI05Sj/phn9/tc3eigyDL1mg0sl899Qcg+lnHCSjqPaWZbBte2V5f+fRwn7B+LBD+jtp6HDDjsjzF1tcTjjhyPRs0+86CIB12Yt+qaGH71Rg4rrSpxkmEaOiPHJ44TNHVRQla0bZqCRh6E2IbO6qnt4Ew687Q1lOP6fPCJW5iFKp3nBciKzEvuEXZQQlxKaHnMYHe1inFmB2MaGte2uoIwEoZsrlYKD8cEL4h46bUNJsMjGgWZxPNDIRK0tUJ/6HA8nHJ9x2KzYCM+l0jTjL/4t36I3dmiCaUc7Od/4w/z9Nt/mEbNLrVmZm4vF6F2nrp5xH5/jK4qSBIEUbJgSJDnORISQRihSDKqJAvjCVkmJy9lhc9LzHuFal3VMgkjITJ1z3O0iZuPRtXEDyMcXyjvtevVpa280vjh7rTMWO826I+n7B0NCcIY29SF12iBMzd0DUWWCYs2g6qerSZbjROuwdlBfyH/G0QoskSa5myuNukNp8XzJNOsWUwcn1rFEgnNNpAQC7mo2pUS+z0Ly9QZTjwsU+agN6I/mlKzRdtxZ70tFrwkvXQLIsvyM96g3OUueFnkec6t/QHDiUurUUGS4PbRUHju+iFqIai10q4voNLynFOM07tjVvtBxI29PrIMSRzx2NP7bK02UVWV9XOY6S9IEp9nJZ3HNLsoDvtj8eDZBkmasns04t7t5cYScYFBncUMmng68lxUQbZZGJaOHJIkYTzxaTQsgfAIIsIkJvfzM953qqqc0U6ZxfFgwmDiIklgG8LpZHYOhJFtXj7gfiCU02YXXZZltlZbHI+mRFFCs2Yv7EpmfcmZ9oZl6AX9uFDjK3CuVzY6XFlvsVO4usyMHABW2jUsU2Ot07wrGKRgLSbIsoxpaPz3P3qE8dRbCvFK04z/8Jt/xDd99ecA4uY29PMTpR9ERHHCs/s9VppVVFX0s0dTb6F/PjMeFrK5CW4Q0mlVSTLhRH9ehT87pmBOV2SG+55Jzz6XSJKUg94IL4iFyFmngW2e1er5WMMydV5x/45QlPRD2o2qQBwZAk4qdjB9spwSZ79M9fC8xBKEMY4flhr5SZpyPJiQFIxP29TxwhiQiOMEqZDLuH04wCiKJkEdXyzSOs0qUYErd1xxX9arFh95Ype94xG6rnDPevcMYuS8UBS5IM6ceIOqd7ELXhbCQHzER2/sYxbaJRurTeIo4dgPSzORPM/pDR0aVXsup4mdZ1DoD/lhTL16VuTvvHC8gInroakaL7lnixv7PfaPR3zcS66wTKYZXqAkPmMlhVGCrip3NWSaoRlmF1mYCgdl3/h0xVW3rVKRL4pTJM6qzIVRzO6R0GQgFwkmSVJsy2T/uKhGVJlXP3SVzZX2XcnAun64QDxw/ZDD3piVtjCAlotEPSMdBHFC+5SolaapS8kPUZxw66BfUtGrhfEsLMe5hmHM7UIbYjCaEoRRaTghy9IdJULnYzZcm1GnW7UKjz69tyBROh9eEPFnj9241Hsf9sYFqy4thKrUkjU6k1qYhSBz6EgSyIrCxkoTL4ipVyzWOo1z+7wgSDTS3LnK88KsY0liu2wlddAbEUZJqQlz2B9z7Xli+50OQ9d46b2bDCde2cqbnaej/gRFVrBnlH4noFGNFu7bixySCo1PRlMX1xfVuGUatGrCm7VeMbF0jZEjzK63VluleNZs1zsr0tI5LZNZURIEEZvdJooic9Sf8PitQ2qWwVqnyfFwwuqofin515nmycHxuPQG3bikRO954QcRUy+gXa+QpILJ2x85Qtfk1L13aoaMLMtsr7XPLbou832GE4+qZdCq20ycqph5bXRI3OUiWC9IEo/ihCASfePN1fZdnXBZlgtp0rRUObx10AdE9TyD9mVZVrhye4wmYjLerlfZWGmeFcc5GiJLEoamMRhNubk/4FUvvcJ9V9Zo1WwGE4/7d7q88v4rd12hRVFcog1yRP/v9uGAqR9QtU2aNZtOs8Zk6pH4IZosc+8lH3phTCtRLXDwUy+kkaSlpVwOmJpgY+Z5Tm/ssbohWln3bHV5dq/HcOJimzrbax0URSaOk/IGrNrmGeLJLA56IzRVKRfE4dSlVjGFVGtytndnaCr3bHTv+J2CcNZTtEiznI1OndHUKzxAM1bmWJmiYhK+kmvdBo2ajaIobK400LU7686XokdzQ7HVmfdnEYIwNS0dflbbtXMF104XGDNNmPgUK/D5DFmWl5oWxGmKfoqBOxN8mvmQCh0bnY2VxpkdmKlrTKaegHZqAjxwdaPLfTurSJLEM0nKarvBy+/fKVsfaZohSyea8jNhp2XXQWgJ+UzHAU/cOGT3oM9Ku0GWQ802uHUwuLSGt66pXNnskCQJ/ZHLzf0Bsiyx2q7d9UARRCtGQiq07oeEcUacpOxcbxPGoj0546HUK+aZ73de0XWZMDSVwcihP5oKUpYq0arVUVWV86YEL0gSv2ezC0jn6jDcKTZWWtw+7OMHITcPBuysdWjV7QLaN6Vqm0zcgFHRr5tpA6926md6bVmWEydi4LV7OCCMY/qjKbcOhYDTbCgWu88NM2rogliT5zluEHI8nLDebVCzTeHuomvcf2WNQdFCaTer2KbOcOwQFJ6W5wldJXFasuAAwZLMcho1i+s7qyXOdZ7dOvsOmqqy3m2ys9Yuq7M0zbh1OCj1m/tjhzTNluLGhd/nSVXn+hEvv759LrJFkiXe+IZPv+P5ygp4aZoJ8fzVTpM0zem2ahiaytRKy3smTlIcPyDLclw/xDI04iQhTXMiEgxp0WlohkX+9Xf9Gf/5v78XgC//vE/kC173igJSdtYDcTB2hbORJWRj945HXFWXw8ZOFxgl8uY53DfzLu7P5RmpWibDqVB4FDrnol04Q/MoskytYhKEMXvHI65sdE4N0CUcL8QoVAV1XSsXpLVugyvrLa5sLlLHZ8iovVNyqsuOv9uq4Xghx0MHxw+o2BYbK038MBJ2h65/1995NPXFM28LqOb+8Rhdu/Og+HQYcwvaDKLcbQoRrzTN6I8cwiim06jSfp4JS2PH5+NeeoXbh0MBPU0StlfbHA8n1M65jV6QJC5JQvh8OJwSJ6IaqNjGpVmOYoi3ih9EpFleGjBIkiS0tvMc1wuwCkdwoU4nIIOnL6hoI8gc9sfCpEI1qddspp7H2HHRVI1m1WLgP7ftmW0ZdJs1+mOX0cSjapm066JymvXyOs3qwjb3sDdm5PjoqsLE9fHDeKlEaa1icjiYULUE/GteoU585/nvKVMxNbwgwjI0oviscevM0HUG9arZpqB1LzERqNsWk8ImK05SJlOPnfU2P/O9X8/f/N5/Q5Zl5ZBYkWV+4rv+2qW0Y3RNJUszni0kWv0golW3uXdrFU1TeGzaL4kY5EJZUkZG0xSGEwfPj0ryjV7MKXJg/3jEcOLyxu/+GW7u93B9oT/9O+/5EPddWeP/+dnvxF4y/HSK+2iGG54p652XGDZWWtw66BNGMTmw2qrd8b6enSug1I+e6Upb+mKlHIRxaRzRqtnntvVmW/iRIxBXO2vtQk42Jk3z8tkwi0HdfMsDxL2Q5hmbq80S/nnUn5ZzkHTJXAlOqONpJpjLccHBmN+1gZgh7Wy0i162ShQluF5AUGjvd5p3r28kFvI7P/PLIssyekOn3HG1GjaeH+EFId1mjbWu2BUoisxq5+5dfi4baTG/eFlFWLT5QcyVjTaDscPg8M9BxfCykWYZN/d7BGHMUUGdX+82ubrRvjTLUVGEbVbNtvCDCMvUCaIYVZULGJmC50dlfy7NltuGzQhBu8dCZF5TVR66b4vx1EPKJVZaVVr1CoPj0458l49uq0arXmHieBz0x+UMIJyTx5xFkqSMXVFNwMlgMk7SM8mgWa+Q5TCaepALbeaJI5QHl235O80KFUvoNhuaVvYk589FkqSlvooinyjjne4Jr7Troj3kBWiKgF/5UcT1K6v82o/+b/z67z7M7uGAl17b4Bu+7PWX7gurqkLFNkqj2rVOvVCuC2jpVTw/5KlbRyVlXlWU0pUljGJcPxRKlUj4QcTRYFIMfiXe8ovv5MmbB8TJSQJyvJCPPLXLm3/sl/nnf/9rzyxWmqYQBPEd76NZiAJjpWRqnidNO4skSdk9GpZJ39Q1gigudW1mxiLb6x3CSFinqYpA4Nw6HCzspOZjRoCzTA19TptGKdAbE8fDD2NycizDWPqdOo1K4bgkF0le4nAwJgdu7g9Z35xSq5gluWYWM8Pm4djhaDgtNXY2V5oL96WuqXSaFYJI2BlOPY+1aoO1lUap4T1jiJ7+jGWhaQr+JZ75ZTG/40qzrNSpl2UZxw+JCnPu56J/dNnw/BAvCOkNHda7jVKm2DJ1mpnN4HD5370gSdz1AvJciPrXbCEhmeUpR8MplcJ67TIxD+1z/ABT01lfEdC+brPG7XBQJsA8zxhPPbIsO9MnM3SNl1/f5tbBgFrVhFwQUq5udE6EgyKhmz1jU92tNoqiyDTrFeIkK5QNBVb39OBN3PCXG6RJkkSnWaVVt0s3eFVJGE4cVtvpmQVRkeULTSnyPGfseNx0hO1WvWLzknvW2S2kCwxNZWOlWfqcbqy02Cj+9vZ+n0eevE3FEufvSz/rVXzqK+/HvAAdcl6oiqDkz3DVQRiTpBlRnHA0dFndEKqDg7FLlmZsrLYE49HQkKU5go0mtEKyXDjV/9J//x8LCXwWUZzyc7/2br7l6z6vZK7Ootusceugz9QLSJOMKEk46I2IogTbNud0S06SxWjq0R874voNpuyst89FyQzGboG0Erufw8GEvGA9glgUZip6UzdAKVT5QOCsx46/NImfEE4k8jwrNEIElM/SVT705CGGoZGlOWsd5YyglF5c66PBlLRQNbQzHVUR8MKxF/D+R59le7VNq2GfQb5EkSjQZuqawjR4XJoGw4monGUIJrXrBdimwVq3Qa1isX88LFmclq6ztda6MCmvzD3zILwJ7rSIzsLxAmxDI0kzxo7H8WCCpqpcL2YAQRhzNJwuSHFcFH4QMXF8FEWmUbXumPw9P+TWwQBD06jZBruHQ65stLlSWCFygbjaC5LEhX5zJhQHbY2kcHSeXey7EDU7F9onsMhdocNyOEBVlcKkdiSGnA0hVhVGcWmYDKInJcsSO2snpA/PD9nvTWh0IvJMJLp5wsllQ5IkVjt12o0KeZ4v1YhQFJl23aY/dtBVlShJaVQvXtjCKBFu8HNqjXdr3QYCurnRbbLazojjlDhJBTQSSlPk83DUcZpybWsFP4xQFEsgbs7Zct8pqrbBxPFQFYH1jpO0dLlHOukxN+sWw4kjSDKGRlRo3iRFovZDwfQdTT2GIwc/WI6cEa+NC/7BYn9Y11Tu2VwpEUyapjB1Ao5HDrYhJHH9IGRrrV2yfPsjp0QjJWnKXmE6sSyCUEBKkzSjXrEwNAXHS0rkSBglmLpWPh+nrdOWXd3ThBMhUCYSqhdE3DwYConeqk27WWM89Xj0mT1MXUfXFCqWUQi5Cf2drDCg2D8eY+gqvdGUPMuxTUEAGjsBFcsvi6Ph2GHveMTe0ZCNlRathoDgZflZ6zRJkmjWKzTrlQXf24njlWqcIFol/ZFzYStj9szPTKHni4BZNX/eIqAWap39kUOeFbT5LGDNqwscvKaU7vR3ivHU45ndYwxNQ9NkkS82uhcOt0eOV+yYVEyjQbVi0qhVLtUKekGSeMUyeHLqs3c0IE1zOs0K7foqEmKg5gcRB70RcZrSqNistO/sBL8sFEVGkqWiHyz6xr3xlNtHQ8H4DBM6rQp5JjTB17qNpVTf4dRDU9WyAnL9ENcL0J+jwNWdkArdVg1DVwvTXpV69WIz6NNxOnGnaVYOjC5yo48K0gsApljQHC+gUbXLiXySiEn9MiMJqzDdAIGUea5Rq1isd3NRzSJoy7ZliIcxO9mlxHHGzrrAGUdRQrdZZeoFPPrUHlmeU6+abHQaBFHM+x97lnrFLCu707HWqVOxzaW8hZkeR47ApR/0J6y0qjhegKFreGEstL4VheP+hL3jIe268CJVVYUgTJburN76a+/mu370PzNxfbIs41UvvYfv+cYv4ZUP7OAUvX1NVVjvNgGhKSIgf2GhoyN8UoGSJCMXMMz5UBSZLMzxg4jbB33kYsg5KRilB/0xW6stqrbB/vGIp24dcXWzix8I6YDrOyuoqloM9JIiScIMnBB6EbcPhqx2hC7N4WBCvWIytg2BYtJkVFnB0vULK+lF9cwMZQ56rGtqSdxKkpTB2BVOUKa+sBNSFHnBHeqgN2JSMFdVRcD/lhVEM8enieNjmzor7TpBEDEYu9QqlsB7F62gPM8ZTlwGYxdJEi3XeaOUDz5+k7hgBzerdjnnOM2IXfjuCPTaLPJcABVm8f85t3vPD7Esg/uvrnPYmxImYlByZWNFMKUOBGGgoqmMCyH5tW6jdOkJ44SKqdOo2ZesNMXJGU08PC/CNjV8PyIpkBC1qnmh8NPzKKkhjuaUKuHprfZMvOeyYegqli7owDOSxUq7Vva4bx30idOMo+GUm/s9dtY7SxeSmm0KSVtLkKhkScIPY0bTntjJFBZhinx2kdA0hdv7fSq2gaZqWAUh5LnGzAVnPkxDo1m3cHxhLKwoMjtrnfKhnFWcD1xdRyk0Ph59dl8wUze6fMNf/gx+4hffcaalYugq3/zVn1tC4pbxFmRZgrxAzzD7rxDSyhORID/y5B43Do856E1oVizWuw2ubHaX7oje8gvv4Lt+7JeFm3sRf/rhZ/ja7/yXfOC//CPu3VoRWutz+kIz6zTHCwQUr+hHzxzt/UjIObdqdgmBE3rwguXrh4KA1G3XOeiNIYP93ghFkqhVhOmGH4r+//FgSpKJoaQsSdy7s8rGSpPdoyFZLqCUa506juuzeyh2L6Opx9jxCvKLwnqnwUee2uPhR2+ws97hofu2L70zNIvWxmzxC4pFOssydo+GpQZQbySMHZYhqLwgYuz4ZfINwpjeaLoU/qcX5zbPhBG0YaiMpi5H/al4JmyjVHicSXBUbYM8F6xfTRXP8UFvVPSyhQn3aOrRrN0ZwdKqV5jsB2SZMGqWZWmB4XvReXtBknh/7Ao9B7XCercpjHAL/QzXDxdcRSqWLv49ry+49DhFr/tOWFLL0LB0McwbT4Wzz1qnxvHQwVBV4jgDS2xLz4PHtWqVcuA3ExRaRqG+bMz6lbIsqqb1AuP8XEMo07VKG7pOs1IOkEaFgmPVMrBNo+j5+UuxxavtBlkuSBOKIvRovDAkiRP6wwlTN8Q2PbbWWmwUaJkZZC2MEiq2cFq6slFjtTBz/lgjz3NGE5epG6BqCn/0wWf5d2/7Lzx584h2o8Lf+arP4dve+PnCRT5NyTnZ6cz0s+u2iaoqfOUXfhK3j4b8yjv+RPiUyjJhHPPVX/DJfOlnvZo4Sc6FxMmyzFqnXtjGyRwNHFZaNWGxZercPhwydjwMTWNntcPU8zgcTOi06rz02ubCe8VJyvf85K8sJPBZBGHMP/93v8U/+3tfg8rZhVbT1DOzjqPhhCiJSyOQwdhlvdtg6gVE8QnhZOoGZJloV22ttuiPHTqGGI67vg+5kC+IkgRFsQSTNRfzoInj0WpUuWezy/ZqC28yEAnseMTV9VaJEBuMHTw/FIxOP0LTVR7c2KJZtznojzEN7VwJhCCMSwOVimWw2q5zPJiCJNGoitlDFKcEYbyAxR87/tLdelLsTMpzpypEc5osWQFjVRUFRZGpVSyadWFnF4YJqqzyCS+7pxCOE270tYqJWxCrysW1uM9MQ2M8FTOKwcgROupRTFPK79ibNw2NezY7OF6IXOSXyw5RX6BKPBLSsWU1KJ0wuiSJfK6XOsPchlEiPAUrJ33fwVi49MxO5gwHPD+gmT18x8NJ6VVXr9p4Qcxhf0KrUcEvWgXnCT9Zps5Gt176c15mUDE7ntFETL0VWWalJViay1QJn6v35ywURV5qO3e6faIocqnbvOw9tlZbJ9VPGGMbBqosBKXWOi2SNBWu9llGq1EVPd2pS7NqU7UNKpZeih99rJHnOY8/e8DNgz62afCffuuP+dlf/T2iWNwfR4Mp/+Rnf5N3/vGH+e1/9R2oioIsnTCCozihZhvIioSlazheyBu/9HV8w1/+DJ7ePcbQNT73U15Ot1ktIHHKhcfdqImd2lpYZ/94RG84BXIalXaJB5clmWpNR1WhURU44tPv+cTN43MI1OJ+/413/Rn/7O99zaXPUxCeaIfPFDTlQjJ2Pqq2yXjqM/VC4lgoOjZqFXrDCXu9IVIu4fqixZKkGa4nkvFMUwgoCCiCRFazDdqN6sKusVYxUWShrnkwnNCoWLQaVRRZQkK0nZYl8RNpCrHj2Vlv025UadbsBQNhSUoXBgEzCY1lz46ha+TMsZejmHax2BwPJvzJR26yO8qoVUweum+TetVme63NxCkKH1vo3xwNJuiqSpxmQjdFlvHn5DySTLR+dg8HjB2PMBI7HnJo2BbXC0mQ+Z7/sjD0swvcZQAOL0gSb9QskjQt+qZilZqtVKYhPBFHUxdJlgtvyo5oaZz6LvNazjOzATeIkCiggY0qcZywezwsRXXGTsBw6mKZQjDK0FUMQ2OleXHf3dDVSzPIZjGeehz2J1QKHPetwwHrnUYxAEtwPNGrC+KI3nCKoatU7cvrLJwXwoRBYEp1VSEuMOFpKgaWlfbFVcHsnApZXJmJIwaGWZaiSMJ5ZOIEHPbHHA+nSJLMdBqw2hG7qfMwxHcbg7HLjf0e7XqFwdjlZ/7Lu0jSxfTnhzHvf/QGv/WHH+SLPuNVbK222Tse4hfaFQ9d32YwcYuOmsRTN4+4eTBgrVPnDZ/2ynLhW1b1LgtDVznqjzjoTcilnNDx8IIIXVeoWgbHo6k4z0nC5qq2IAE7C1WVz/St50NX7+6xtAytbINlWUaeU2rtz4eiyGyvtxiMXW4fDqhVDHqjCcfDCfdtryHJYhA7dUQF36ia1CoWXhBjW3Ms2d6Y4dRlNRKV8+2DAWvdJmmWUTFNttaaoqetiOdz1n7LlphngFiERO9ZnKskFfoz17ZXz7xe11TRb3cC1EIz57SO0SxMQ2Nzpclhf0IW5jSqVqHHHvGBx29hGCorbaH8+OEn93jty6+hqkq505m1dmcm7iYCUbe10sQLwlLczDY0RhOXx57dxzJFOzHLM/Is55UvE4zW3nDKYCxQact8b7MsK52vjAJOeTQYM3XFrnjtgoHuC5LEVwpPwCCKS+GmeUXAmTflzF9ztopVbZOpF6IVJIJZ3xfEyhpESbmlnFlQiS3kCWRLkQXkp9OsPa9U6OHYoV9cpE6jQqtRLd1MFEVGoSBQpMIY+KnbRxiqSn80wY9iqqaJJMtU7XApsecykaYCRrl3PKJiGeTkOF5Aq17BC0LSLGNjpbG0FZRlGUlBm56dl1llPp547PWEB2ea5uR5QrNuMZr4aIoKkng4946HrLTr7KydNYG924jjhN5oimVo5OS898NPoygKSXqWfOz6IT//397DF33Gq7BMnWuFrvkM/WNbBjf2e7zhf/8xjgYTXD/C1DW+40d+iZ/4P7+Ov/aln3bmPZMkpTecFiYgOp1mtVTeHE59VE2ialmCietHyMg0GiZxmnDYn3J1s8vOemfp7ui+7RWxLffPDn9NQ+OvfvGnnvm9oPSLoatl6gvDuZVWnTQdzTna185FNciyeHbqVQtT1wTFXBJqh82aTZzImC2NasWkPxKs4XmpgTgRbQXbNDB1kSSPh1NMXcM0tVIMSlEUdtY73Droi2SXiSS6bE6SZdlCgTbTVVpWhc5gibWKkIM19It1jCqWQatewXF93vOBJ/j9932ULMu5vrPGZlOcw6pplJK784v5jHG78PlIqKpAwczyVxBG3DjoU7FMwcPwQ9aadYxCvnfiePRG0zIHHQ+maKpSntOZYuRoUljCaSq2qRcOY0JCebfQPFoWL0gSV1WxbT2vTyRJ0tJh30a3IZxa4gTT0BaIAzPX8tlrJUnccGmWochC+0HYqCWCEvw8JvCp6wvX8uL77B2PcP1IOP8UECoQlYimqdiWjm1quH4orJeaVcE8rRpMXZ+wUb1rqnAYxdw+HBQSvUJcqFmvkBekmHu2VvAnvaUD0xl0UAySYK1dKyFfw4mLbRustOuMHY8kyVhp1ahYJsOxh1GILsVJgjcI+Y//9T38x7e9h4nj87Lrm3zPN76Bz/+0V97Vd5mRRA56I3w/JI5TAj9aaLOdjnm9FsHWW6zgvvLbfpKb+32S9AR+CPDNP/gfeNWDV3nlAzvla/M855ndHn4o2glBlBCn6bkY4TyHZt3m6maXq5tdoaJ3yvBkNpSPkxQviPipf/BGvubv/zT+XF9c11TWOw3+zld+9sL7p2nG7uFQDC4BkLiy0SnvEVVV2Fprl07td9PKMnWdHOE9KxysIjZXmtSr9gITeqZFNJy6HPXHCy05q8B2n/5cQ9e4Z3OlhPzNXIJOh66pyJJUuHWJ/vIMonleyJKEoiw3lZmP48GUw/6Yv/X9b+UjT+6W0si6JvOK65v8+JvvwQ8TNO1Errr8DFmm3ajQH7sYmkqSppi6hlHIRs+e95HjUbFMXC8qUTUjJ+D+thi2un6EoWkLO1w/iMv8NXUDDnpj/EJaOwgjpBxeeq+YpdxJeuHP30AOSjbb3YYsyzRqNivtOlXbxPEChmNHKLjpwlkb5t1iJExdxfMDbh0NuHUwYPdwSH/klnClWVzGLzrLRKXbHzkLD5/nR2haodMhSQzHHjf3+4U055DD/piJG2AbOhXLKNxURF9RVWR6o+nJOSnYbRdFkqRMXQH/S4ukdNAbi36sbWAbBv2xUy5Yd1KJ3D8eFUJaBhVTKwyok4Xp/j0bXa6sd1ht1TjojXnq9hE5YvGo2gYVU+c7f/Q/8ZO/+A4OemO8IOJPP/wsX/X33sK/+s+/e8dzO4sZqaJqGeysd9B0sTX9xFfee26bxjZ1/vLnvObc9/zQk7d57Jn9MoHPRxQl/Ph//O2F3x0PJzxz+4iJ43NwPIZiRzNDEwmcvzAT7o8ddE0pd5e2ZaDr2kJyyTKRhIcTlyCMOR45fNIrrvPff/rb+QuvfSm2KSr9N33lZ/HHP/89Z4bcrh/gR4IHULVNVFWmP3YWXiMVO6jLJHBZhhu7PZ7d6+EHEeudJs2ahQRlAp+95yx5HPbHjB2fiqmjqio39gdC48QPadXscz9XVRVsy1jYbS97jWiZSmUCX22f7/N61J9w62DAQW/Ejb0eE+eEji4kicUCk2ViiP8Tv/AOPvz47TKBA0Rxxgcev82//i/vIk4TXn7/1tLv0G3VWO/U0Qslza0lrlC6qpbnTpLA9WPW2vVSV0UvFoBZCLOUk8+K4oTB1MM2NCqmTtU0GBV2dlDYR16QM5/XSjzLMr73e7+Xj370o+i6zj/6R/+Iq1evPp8fAZzgP4UhsCyw5o0Kuqrh+AEgYRo6e4WSn+MHTB2fZk1cBFVWSqjR1PVFz6zY7p2HSc+yfEEkvzfMS9slTVNI3UIhLozxgoCt1XZpPuH4oWDtFRNtU9eYugJ/PRMqmno+rh+WK/15UcrPpkI21dBUdtY7hHFM1RKqiK4f4ocx44LwsawvO38uZ9KpQPndkyQVhJO5G/Ydf/wh/q+f+jXCMCEHbEPjm776c8iynPc/doOnbh4RnHJk8YKI7/iRX+Jr/9KnXoo9l6RpOTM4GkyQkUiR+AuvfRl/9Ys/jV942x+Vg00AVREyqH/ls09cwGciRVGcYFs6T9w4PHfnlWYZDz92s5QCTpKU3sDBMAREMsvzwtGpWia1rdUWtqGV9oAr7fq5yoYgyFizJAyih717NORl1zf5zZ/81juSxgRmeA63LstkSxaky8SkUPXcXG0xmnqMHI+X37e1sEObOj43C4njtU6dlVaNqRuW98iV9Ta3bhoossRKq3HXPIZlISRkG2Ur5ry4yDmnP3JE31kSSoir7TpJkvALb/9jwvhsGy7J4L/9/gf48f/z6y702pyRkc6LVl2AI/wwplGz2Vprs9E9MYV54uYB/+ytv8XN/R4vu3eTb/iy13P/lbXy7w1dg8LcgiwvPGibJFnKyBHt0aqpn8uBfF6T+Dve8Q6iKOKXfumXePjhh/nBH/xBfuqnfurM6z4WeB6IRDZxghL/mWUZw6nH9Z1VkmIlvnU4KPUnXDfA1JNyWh8nKWmSlQpus9eNHA9FEdKep1EuYbzIikzTjOOhQ71q06jaOG7I1AvwwwhV1cobW1MVIfA1l8BmA5c8B9tss7naFDKyVftc6ddZDMdiSDd7oFw/ZOJ4WLrOYOxSsQzWOw2GY4eNbmOh9x9GQqQ/S4XIzgxnb+qaQDgYGlGUCIPo4r33eyOqlsnDH73J3//n/3nBNssPY/7Jz72Nf/2938Dbf//hpZA5ED3O333vo/yl179q6bWME+GlahaGBnGccjgW7EBDV0kzodHy0//gr6MT8wu/83CpqfGXP+s1/Ph3f1051Z9VvVEitubHQ4daxTwXkQOwtdrixn6v9ExVVJl2vcIHn7jFH77/cfoThy9+/aup12zW2nVUVaHTquMGMT/y736L3/qDR7BMja9/w6fzDX/l9ct7tPnsPzm9oUvAQCRiWeL69upCIkzTjN/6ww/y3keeplWv8CWf+epi15OgKBJ+GLHRbRZyBAK9dVlLuYkTYBp60ZMVffl0bkHw/JD3fvhpZGQURVTgr7x/G1k+kZiVZZl6xWTzlGvPc42p6/PUzQOOhkKs6/qVFbbX2qXO/nyLKM9PziWc2BU6XrDAlJ16Ibrmiyp4SQKfRW/oPCc9lJlfbZ6LYff2epswEiSoeZ2XH/hXv8E/+dnfLIubR564za+88338/D99E1/0Ga8CxLO8udqkPxK7uppl0G5W2V5rs388Eq3Rms3+rcnSY3lek/j73vc+Pv3Thdzoq171Kj70oQ8tfd3d0tVPRz7n3g0nFkiz9z594Zv1Cof9Eb3hhDBOieKYl1zdIE6SkjQCwnVnNHXx/KjoP56gXE4PWSTppAWjKDJba028QLDsqtZE4NkzMaBZbS8Otwxdo1YxS4Ge3tCh26hiGufTgmeRnBLylwsoYBBF9MdTbh/2qVcsXvHAzkJ1GMUJ+/0J9U6MIsvs90aAgM1trDTYOx5xNJzQG07pNKo88uRtDF2j26pxeDzh+3/615f6HoZRwj/9uf92YQ9/3iZtPhwvYPdoKPQ9MiE322lW6TQq3D7sIwGGobHdqeN6IfKKxN/9ys/gn3/X13M8cpaq+EVxSlC0eEAsojtrQoPi8RsHZ1AhpqHxt7/yswrdGF9AAmWJH/iZX+e/vuvhkhj0tt/7AK9/7YO85bv/Glc2u3zw8Vt81tf/34KuXywQb/6J/8LP/fof8O5/+91nFu0Z32EwcXnHn3yU9bV1PvHl1zB0laduHvLSa5vUK2KY/wVv+mEO+wKvb+gq3/MTv8L3/p038BWf90lkWcZ6p0GWZTx185Asz5AkmSsb7bKaTgudmdlnL0jMKhJRkqEVA7w8P9l9Ce30CVmS0m4Xlafrc/toyEvu2RDXCqHFUqsYz0sCj+KEZ3Z7jJyATsMmSlKe3e2hq2ohsCbIL+26TbdVW2pXWLFOVDlLuYSC+d1uVNF1rUR+nI7ttfO1UIQB+8nClWUZnh8SRDHDiVcYuSvYpsbGSuvMM/DeR57i//6Z3yCaI5clqQAQ/NXv/JfsvvNfYBdaMjvrHdIkJ8kFqWljpYVamKZbSwzg5+N57Yk7jkO1egKdURSFJLmc4endhK6JysMLQuJEQBXnNbdVVSEv7Llm0axW6I1c/DDG1ESLAxa9+OIkZeL6JXHCNjWOhlOCMC4sp+TSNkxYYokb3Q8ibuz3BfRq4rHRbVKvCC/JjZWzVH5FERetXrFLKzZdV9nvjTnqL19tZ1GvmESxqMDiJCUp1PtkSeb+K+s8dH2bVqN6pm836+Gbuqh2bVMXJtAIAsnWagtT07jvyhprBVXdLSj3Vzc7pXHCsvjQE7f57E96GeY5baA4TvmMT3jJwu9ES2yMbehULYOqbdAbCTRIq1HhynqHnY12uWPR1LkHVNdES2Opeh+n6Mti8f2Vf/HNrLXrZXJXFQldVfj2v/6FfNwDO8iFUp8sy/zXdz3M237/gwvMzjBO+f33fZQf+Xe/RZ7nfMP3/CwTN1gYqPpBzFM3D/mxUz32mVLmf/jNP+JzvuEH+fdv+xP++b//Lb7i23+Cn/2V3y+YhBMmnseXf+uP8+zuMU4hWxBGCUEU830//evcPhxwZbNL1Ta5eTBgMHEZuwH9scPjzx6U/eCb+z1uHQ64ud8X2t6Fnn2SpDSrNmma4XgBUy/A0IWR81O3jnjy5iHHoynZHFQkL/7/zDRFkSVWOw0qls7u0ZDdo+HCfOhuI0lS4jgpmLJyiZgRdPlQ3BuW2GU6XlA651iWjh8KkampG3DYGy8k6hmkOM0y/upf+lR09Wyq0xSZb//rX7D0uBwv4KlbRzxx64DHbxwwmXo8u9vj/Y/d4F3vfZT/8ciTYgdsaHhBXEJ6y8+PE/7lf37XQgKfD0mS+I13/RkgdiIHvTGVii4E5DjpAFRtgyiOF3ZLp+N5rcSr1Squ65Y/Z1mGugSv+uijj37MnzXziYzTVKiPudaCXOzECXi6OLGyBFGhk52nEk4Ih4d7rHdqOH7E094JjTtOUsaFzyaIoeV0cIQiZTjDI0ZTnzTNRL/UH7F3O2f3aIyqyiXB5ENJiq7KZJlgnLZq9tLhYpykHB6NsC2D29MBAE/7EdtrzQWPwChOCCPhZSlumohbri/6dVWL24U0wWy67gcx4/4hjepJ28r1I8Ig5IknnyzeM0WVJdzxcXksu8fjEgK2ezwmiRP84rjEzmP5tVAUide+ZJVaxSgQQScvNA2VL/vMV3J8cJuj/ZxHntxnrzdhZ7WBbS22mVw/wh/3MHSV4dTjmYmPJBXO8J0af7y/y1F/yM2DIY2qtfD9ZpHnOb2Ry1N+WFZP7XqFRtXkN/7Z1/OO9z7OB5/Yo1Yx+YQHt9lZa/HoRz9KGCasd+sMjjV++K1vO9PbBzF0feuvv5sv+dT7eezp3aXnwg9j/uUvvZM3vO6+hd//5h98mB/9979dLgxJKhLOr7zjT3n7HzyMH4it+HnwcT+M+L63/DL/7Fu+lChOeN+jt6hVCk5BnvPExEeOpzh+hBfGWMaJeNvNmtC+ieOUvOgX6wV6xpHgI49OMQwNWZLoj10OexMO9g2QhPHItc02v7e/W0B94ZEwIgojeiPxfMVJwka3XhKO7ibiJOXZm8c4BQEwilOSNGFfVrEtFUPTMA2VME7oHRm0C9XPNMvYOx4jyzLuRHhaTly/sCKUxd+0qvhBzF987T386SNP8MEn9pjRDBQJ/sIn3Munv3z9TD5K0ozbRyPSLGM89YnTjInr065VQBLPzmji405G3GrYWIbOnqmz1jnZcYdRwjM3z5evjuOYD3/0KR69p8HtoxHKXOvW80OOD2vlszh1A244Pp3a8pnS85rEX/3qV/O7v/u7fOEXfiEPP/wwDzzwwNLXPfjgg8RxwnDikWYZVdu4cDD0XGPmME+e874PP0MYp6XEZhgnXN3slkJTM5Wzw8G4oA0b5HmO44Vc2ejw9FNPct/9959xZw/CGL3SLyu8OEn56LP7XN9ZE8a7fkSnUVmKF47jBKN2vDB4nHoB17dXyz6254eivy9JZDlUTI0HV08c6V0/4ImbhwzGLtvrndLs9+pmdwGmmaYZY+e9rG9uC1YssLN2IpGaZRn27SM0VUVTFTorDvu9MTubHfJc4jNe81J+/08fO5PIZVniDZ/9Gj7rda/lXT93P9/+w7/AO//HR1BkBV1X+PY3fgF/7298IU/eOuJLv/lfFCgaiTTP2F5t8ZNvfiP37awSxSlZni0oJAoDg6zYNkfsHQkSz/33XcfxQ7YKedX5ELjtkNHEQZaFf6tbiEmt1it86yteXlb0At104qhULTgGx8NF5Md8BGFCpdk9N9kCREnGgw8+uPC7v/wdb126MOSAFyTFsZ//nnkOez2HBx98UAhA+SqmqWJo4v7trMQ8+OB99EdOqbkC4AWh0NLRVGzTKC3k7tnqYuga46lHfQ4em6SpAAzIMlkOG90GQRwThkk5cL+53+P27Vs89OBLinMSU7GMpfoll4mdKw6PPrMvpHZlmU7TxnUj/CguCHAGtmUsIGfCaPG5A/Hs7Ky1UVVhVjzTMT8eTPmxjW3+7NFn+dCTu9QqJp/9yQ+hZx4ve9nLzhxPEMYo1hGDscPGho4sSzx16whZltjoNknzjJu7A0F+Mw1kQ6fWqrOy3kFT1fI+ev0nPsn/+PCNpfLHqqry+a9/LQ8++AB69QhDO4FLTr2Q7bXWGSDA+973vqXn73lN4p/7uZ/LH/7hH/JVX/VV5HnOP/7H/3jp62Y2YFmB4Z44HuvdvIRWpWlWAunnNQruNmYkodsHfYYTn954SpblPLN7zCe/6r4S7znfy1pt1dmLRyXKZaVdQ1Fkbh8N8bJbBHHMRqdRsMmkwpUlIU1VFEURgkGyfIIlNTUmrr80iauqIqCSfoimKERJSr1iLiApjgaTsgUClE4/FctgNHH5kw8/TQ6MJz79gcOD1ze5snFWv1qwvmpsdBvkOWcII8LAVrAdwzjBtgxe87JrgKCT/9Sb/xqv+7ofKDVrQPQdGzWb7/j6L+T2oeht/9C3fzWNqkWSZuUQMAhjPvPr/296Q2cBPvnkzSP+xpt/hrf/1LdhGQZbK4t60fM9V280g3FKhQ2aiudH1CoW7//Is/zAv/oN3vOBJ6lYBl/2ua/la7/oU0kzgcluVC0sQ+N4OEGRT3D71QKyNx+SJNFtVumNlifyLM/5or/zw0t9RGdxWnY2z3Oevn187usvE5IED9yzDoj75spGh4PeiCATFO/1lQa6plKxdI6GUyHjm+ekaU6WUfZVZVm0peIkLXXh56GbSZLRaVYXRKKEX2s+dyyL27Isz+7K7Px0tBpVPvHl9woZ44I01mpU6Y8cvCDicDDhwWsbCwu2IsulpLUsy6Vg22ko44w8WLF06hWTv/iprwBJFFDeZPk1nLGb01TAc6M4pVmzGbsuYRhTrRjICowcn5VOg3ZDSBdkWU69ZmFNfbbXW/ytL/9MfvQ//jZxEp55/3u3V/jUV90PiH7/0UAwttM0Q1Plu9rVPK9JXJZlvu/7vu+Or/PDaMEGTFVkBmOHRs0mLogncaFgVjF1Nucqz/Miy7JysGjo2oKy3dFgiqJIbKw0AKE4J7Fcw2BmGzWT9VRVhZt7fcZOiF0X0pwCI51jmQauHxLFEc/uuXSbVZI0ZaV5wiSNkwztHMjUjMA0nnoEUUKzZp3pn6dZjqHNDTLl2cOZ8oGP3iQIYrrNGt1GlcHEpWIb1Iu+5wy5UMp0yvKF6ojL2I6zqFdt3v+fv49/9ta38avvfD+SJPEVn/da3vRVny0EiewTyrTrh9y7LRKZ54f8+9/8I7yCorxwzQoy0VO3j/nCT/+4c49rdl3m+4KzY3z7uz/IV3/HW4RLTZ7TGzm85Rffwe+850N8zzd+MY/dOORVD+xQMU1ajQqOF14IFwP4u1/zufyTf/Pfzu31TtyLZXZPLwxZlhc+r89dntcydL7l6z6v/PnqZgdDFwqFmioMOmbGI2kmzqskiXt+4gZCl9zQBIeCE0f6imXQqFpMHB8kCU1V6DZr/NmjN/h/3vsRTF3j8z/tFSRpXirs1asWpqGX30eSuGsBt9lsSS3mM6qqUFNFkk7TDHIhPNdtCi/OlXZj4X5UVYWNbp393rjUW9lcaZyREZ5FxTa5tr3KcCLavRvdJs8G46XHNlskDwdjJq6PrmnsrLfpBAK1Np4GrLbr5fxo/2hE1bZQVLnwzg3xgoiNlSbv+rnv4kv+7o8wGDtIknh2H7pvm1//8W8pv0+zLhBpM8/dGTv4svGCMDZFzA9PTpAfZYul2EpMvUBgqi+4SbIs4/bBUDCeyJlntM2qft8PsUyxNbJN88KtqyRJ5SIgDI6DwnBAHFPFNjk4HtNpVmnWK9Rsk9HEo1mvsNKqcTycMHECJFkq9LCb536W8PM7X5e8Vbc5GkywTb2gxQtthduHQ3rDKWmWczAY0WnUkIubZOr67B+PQQJFlthabS9Fj6SFKcY8m24Z23EWW6stfuQ7vpYf+Y6vLX/n+SG35yjBqiJs8fwgKqVJ3/PwE6Wn5elw/Yif+9V33zGJN2s2rhfi+WF5s9dsk2/4hz97BtoYRAkfevI2X/5tP4WuCQja6z7+Ab7pqz+HT3r5vRd+DsDf++tfwHs+8CS//YePnCtUdVHMn+t/++vv5rt+7JcvbShwOmZwtX/4pjeUlRsUZijrnbISnYUkSSXKZ/ZMWQVnYjbMX22d0PJnNPZWvVqaM3/pN/8L3vOBJ0vd+O/8F/+J//2v/kX+j6/7fCRJSBbH7pBuoYR5N4p7IDSFDnrjcs7SrNllKyaMYsaOz2DsYuoalimMN4wl71+v2piGXt7HB/0JaZZTMTXWu80z3ADT0C58FuejUbP5lFfex+2DATmi1Xpte1Xoyhctu93DgdBvzzPiLKU9EyHjBLn2ivu3+b23fjfvfeQphlOPK+sdHrhnvTx3s2uwTHr5svGCJHHL0DELoXSl0HLYXG0Cwphgnv6qyvJSpt18eIHon818KcNI6G5sr7Vx/JCVZo1nCnajF8ToqkazfrkTJkkStqETJWlp0JvnuVDKm7tJTFMrK/f1bpNGNSJHtBw+Fl++Vr2ChBhumIZGt1kjSQXGfWutzeM39gkigR/fWWvTrlXYPx5jGVqp9bF3PDzjcRnHCbdmVPssp1YxS3nZuwlBmRbnXFNlhhMPp7ATu3k4YGetxeZqC1U5/zq+7d0f4Hfe8yE+91Nefu7nzMSbekd1dtbEovQnH3rm3OQ4W6RnxKB3v+8xnr51yCO/urzFNx+apvIbP/4tVF77txaYdpcJ29T50s/8ePojh5/9L7/HP/pXv760Fw4zK77ly8Qnvvwan/bqB+g0a3zl538SVzaW69Gct+DOX0fheNM5MY0oWoxBlFCxdFZa9TKpv+n73sofPvxEyRactY1+4uffwasfvIe/UjBjtTmhqNNxemGZjzzPC1E4vXzNaOrRalTQNZWj/oRus0bVMnD9iCAURsXnkbV0TSUi4Wg4xSraQ14QcjiYXNpK7byo2CYPXNsoYYbz+k5V2xRSuUNH6IfnIgcEYYwsn7SvsiwnjBNe9/En80HHC4t21vMDDnxBkrhSOGyMp27RRrCZuAHHwylJnBbWUKKCipP0juYCaZoVFfjs/aWS0ZYU7KdaxeRoMCFJEq5udEpdiGWR5zkTxy+hhZurLZpV4SxvaCqNmk21VSObE8iJ4qRcXWeiS89HSJJEq1GlVmCIZ/06JNH+MDSNydQjTMSg1jT1UhYVxE0+c6uZj5nF1mzHM3ED6tXwrolYM8r0UX+CH8YEYcz6SrP0ghyMXL749a/ix/7Db5/7HnGS8n0/9WsXJnGgROfMzq0XRJdedNIMjodT3vHHH+GL/8LH3/H1kiRxfWeFjz57cKn3B7HradQsPvHl93LYG/PD/3Y5ygVgvdvgb7zh0/mRf/t2wnhxoaiYBt//zV/G6z7+gbviVLz93R/kh9/6Np66dcQ9W12+7Y1fUH7X2e4yyzJu7fXFXKRQP0zTEdvrHVw/5D++7T0L9PRZeEHED/7sb5ZJfFn4QcTe8ahAby2vhmf34awt6BU7tkmhcR/GCaauYhqCJTn1gju2FoSH7hzfwzSKmdbHHjMSEUC7UVloR7Ya1VJjyPECJk6Aoguy4Ox7S5KozGcLwaydJd9lsXRRvCDaKSBW66kXMnV9Hv7oDfqDKbqqQIHzDaKEqKjQzzOZnYX4d0HVTtIUP4hLBlytYhInKY2azQP3rHNte42dzc6FD/9Rf8J+b4wbhBwOpvRGU15ydZVPfsV1Hrq+xf1X1rj/6jr1iiUszPyITpFon6+I4gTPF8iCUdE3fnavx429HpIEmqJwNJhwz1aXB+7d4C+85kHyQqhILiRFoaiQl7jVxHGKNqefrsjSAqvxMloyszB0jZ2NDtd3VoXmtqaiqDLVAsvbbQldkIvig4/fuvTnzeLVD15dSiI6L/ww4bf+4JG5YfTFO7zv/JtffCl3Il1T0FSZz/6kh/j1H/sW9vtjDgbjM8l5PpIk5R++6Q385lu+ndc8dA8SAgr76Z9wPz/1PW9kc6UlnOkvGd//07/GV3/HW3j3+x9n73jEHz38JH/tu/4V3/1jv7zwujhJiZOsbJ9VLAMvFCigvaPRArT1dFw0nBUOUgM0Vej3BFHM4eAs50GYLxg4XsjxYMozu0dCL2cwpjcUSn9e4YU6k2C400ImztNJQRVGCab23F2lZuH5IbuHw2KYLrF3NDqj+yRJYvAZxalo/TSrZwADwlxF6MyMpz5VyyBK0jvef5eNF6wnftgfk6ZZYWOkMnF96jWrlJu9tnXWjPe80Ivt4vFgQpplrLZrZX/JtgSldTB2yXPYXG1eWG0midArqBfaDKYuqtQ0yxe2j73htPSRtAxtoQf5scZw7PD4zYNyeKcpQmBJLswZjvrCdftwMEZVVFZbFvWqhRtEQtFutcV+b0RQJPCZie98VGyT4+GUmi3YrlmWYxo6QRizfzwkTlMqlsl656w63XkxI2vMKPztZo04yVFkmTd95Wfxk7/4znNbKsv0N4Iw5ngwIU5T6hXrjBtRo2bzd7/6c3jLL77zXMr/wvFJEoah8exeTxxHLvw7z1t8v+YLP5mPPLXLj/773yZaQlpTZJkH7lnnX/z9rxFu8sV9NXY8JOQFstnp0DSFZ/d6rHfr/F/f9GXsHQ3Is5w0y7AMldV2/cLzHsUJ46lHnsNo6vJDP/d2glOtJdcP+fGf/x3++pd+OvdfXSvPQV4kvJk6oRjyCwnbi1A3mxf0k6M4WRiY2qZI1MvkZNc6DfJ8zO3DPt1WjW5DIMAGE5drm12yPCt1tLdXW3dM4qahFbOoKeRCr32ze/6xgigiHS9kOHawTGPpzMjxQvQ5dUNdV5m6wUL+CMKYG/u9ApKZM5q6XFnvLLRQaxWLa7rQFH/k9m0+8FGHim2wvdriFQ/sfEztVngBk7gXRlQtUSVLgq9QOLLIyNLd+1qahqgGl0WtYi3FEwu7NUrN8svGzHn7RKchYDB26bZqpSj/c3W2iaKEhx+7haqK/vpg5OCHMd2WEIU3dSHyZRga92x2mXohhqbiBRF2oT2iayrXtlaKSf1yGct2o1Jgx72CUSgIRs/sHqNrKlVdkIpmJrrzEUYxh70JUZJgmxqNagVNUwoMfoP94xOLt497yU65k/qcT3mI3/mjDy2QgcR3Uvmbf+X1gHgowigW0qfDqcCtK8q5kL9/9M1fhmVo/PN/99+RZIkoSpaKHYG4Rz7zE19aKjZmWcZ+bywgnEseJEmS+IH/7f/H3/mqz+Yf/uSv8PNv++PS5ahmm7QbFX7jx78FOBEM88KIZ24d84HHb2FbxlKpAk1VeMNnfgLkYkjYadiMJy6NWoV61cA29Ashe1GccGOvh1wIcv2bX3v3uQqPaZrxn/77/+C7/9aXiM/WVLrNGr3hVPTkyUuxpkbN5os+41X85u/9WenkU547XeUbv+Izzz2mmZNNaWKdpAss2/mQC5erqdc+I84mEDUt1rt3drTJsoze0GHqBeiqwuZKs+Q5XPT8zfS7j0cOrbFDOpwWRtGLx6KqcrGjPdHlOb1TGUyECNq8ibrjBWfmBWma8fiNffZ7Q9q1CnGScvOgT71qcd/V9Qu/553iBUvilq4TRDFqQXfeOxxiGhr1isXGSuOOkMKPJfI8F9N6VyBIZEm4Bxm6hqoq1Csmg5GLZWnESU69YjL0To4nCOPStQQoq8/h2OF46JAD9YrBWufuv4cXhERpTLMuknZWsTkY7JMmCYqu4QcRdjE0Wes00DUPP4hoVO2F3YBAmZz/OTP87LyxhueHQvNcnVVT+plqauapKUsymiLz+LMHqIpQ8mvVK6y0a1zZ7BQUdmnhQXzLm9/Ip3zt9zNxvJIibVsGD1xd49v/xhcydX32jkfIxcLoeiH3bq+IQZKln6E2z77nP/jGN/D3/sYX8eStQ+oVkx9+69t566+/myA8SaCmofIXP+Uh6rZJkiYYCOngKE4YOz61inmuFsjWaot//X99Az/4LV/BL//On3A8nPLxL73KF3zaKwXLNxaDtVsHff7Gd/8MB/0Jnh8udSjXNZV23eKNX/q68t/9MMaPYup5Wmj7eGwuMb+YxdQNkCTKxTGc0285HXGSMnUXWwCdZrU0wz4tnvVT/+CNPPbMHjf3B2XrwNBVPv/TXslnfuKDOF6wdCdr6Bor7Vrh9CSVjlznRcmRKPRhhJKmOddLvnMVd9SfCIlnUyNOMg774wWy2HnhhzFuEFEpfWdTjgeT8nvNVDAnrsdwKjT0FVUW7vWnECR5LowiLoo8z3l295ibewPGEw+9IARlOaXbzyyWIcbuFC9YEl/rNrix1+Pp20eQw/1X15Al6czw4H9GeEHE1D1xwQ6jhOPBhO3CSXzqBnhhiB9EXNkU7iyj3smAy9DVhbbAzDfwqLgRJEli4gZoqrqU5HNRyLKEpigEkRjw5OSstepESUaUhpiaxlqnUbxWXmp4fF5kWU5vOMXzxTax21qc+p8oxZ1fTUVxQprlWJZgtxqaVrJuhwVOvWIZSx+k7bU2j/zqD/BvfuX3+NV3vg9dU3njl34aX/n5n4Shazx166hUlJQliaPBpMQ3J2lWWlid9g0FcU2uba3gBxF/88tezysfuMJbfvEd3Njr02lWeeOXvI6v+sJP4aA34tm9Pte2ukRRwt7hEBnojx3W2vULMeTdVo1v/Iqzvf2Z9sxXf8dbeGb3uGRzRkVilSUJ01CxLZM3fvHr+JLX3cf2mnCHr9oGYRSTZym2aaBpCsOphyRdfvF/7cvvxTZ/v+wlz0fVNnn9a1565veifXB20WrVK7zvl76Pt737A/zqO/4URVX4ss95Da98YKfQ0zl/+N1uCNarIKwoF+5uZxyJwVhorLcbFdqNSsG29ZBkiXa9sjAPm13/OE4II9HGWGnVC3KdzNRLSuz5nWJBFEySiOc2hwd9wdo2dY1W1SbJMja6DSqWeea+a9Vsbh0KGOIMW1E5dX4cL2DsegWAw8fzQ+IkoWqZNGqLInW35xBj1Yp5KZevFyyJ65rKWqdOGMbUqhaOF+L6AbvHwwXz4/8ZcVonWy00U+I4Ye94hGXoVG2TIBTGrlGccjx0aPfGNGq2sHyqVRg5HpIk1A9NQyOK44Xq3PNDuMskXrFMttbaQtCn8At9zUP30mlWL2yPzIfrh0IOQFPKRQWgP3ZRKw6mrgrFwtGU6zurWKZAexi6RqdRpT92SgLFzvqi2a4sS6XpRhSlSLKEUmzrJYkzkq8z6YOZwFGzZvOtb/wCvvWNZ4WHxFBW3JKmqWGbBmPXI04M+hOHqmly2J/w7N4x22vtsoIUsLUxo6mHLEn0Bg6vffk9/OZPfqtQnBu71KtWUSRUCaOhkKuNU65udgTyJxOksFrFek6tsMee3ufPPnJjKR0/y4Vbepr5PLPXQ5bup9uqkmYpUzcEJF56bRNJEgPozW5raRU/O0dRnLB/JKR6W40qn/Cyq1zfWeWxZ/YXKN6CI9Di8153MerndCiKzBf/hY/nU191v3B2KhLpbEZzUeiaumxtWBqyvGju7RaDRENXyRO4edDn6kYX09DKFsjY9bm93y/bPZ4fcmXjRGLiMnnD0FQUWTpxMwoiVgsPyzQVNngz1Fa1YhZCYdqZ+8IPItIsY6VZK8W4dE1jUqhhzo4lilNatQpZBhPH5bA/RdNkXnZtk/VuE8cL0FRFGH3kLCDG3Esgxl5Ask8Be9I1BmMHxw+RkfD8iP3eeGEFmtHwQSTH2cl0vADHDVCLbc5l+9qGrkFRaSqyhBtEdJvVcks6e3/T0OiPHJEUowTXDxm7Plc3Oqx1Bd02R/Q4XT8kmevbCqPZu99RKIrM9e1VOo0qUSI8Q2f9/Musa/2Rw/FwUuKyW7UKa92GUK7zQ65bBkeDKVPPJwhi8hzu2eqWn9Ft1YRMbqFZcvqcGromjIsnLn4QsXs0YGu1xWjqosiLW/PB2Cm314amip7lqd6zH0TCV7C4hv2xS5alTJwAy9C4ut4lTlOiJKHTqDLsiznEbOc0O9fj6cnO6tp2l9uHA0xDwzYMttZa3NjrFZWxRJblbHTrgrhSnlsZpLyUB77b+Oiz+2iqcm4/fiZB+pu/9zAf/OizPPKrH8fGSou1TkanWeF4OMU2ddI0L23AlsVhf8zUC1lfaTCcuKUN3jt/9v/k677zp/nd9z6KrmtEccInveJefvQ7v/Y5F0SNqlWqHYJ49poFq/p46GDv9ahYxhkN/CQRcsCSJJVGKJcJkQTVcpApho+CH+H6IW4g5iUHgymqIoqHKEkKBIjQ3L6MreEMFrt76yZKgR6ZASEkCchPsO7nOdQPxw5Hg2k5HE7TBE3TS9elKE7YLGZJQnNGYnu1RadZoT9yWevUqVdMQXqShRRzFMcLhJ/TiLFzv89lTu7/rJgB4nvFkDBJUrbWxMrkeoEg2GQ5w6nLrHth6mLr6vohB70RmqaSehlT1+fKRvdSD6CuqWyvtjgaTAiilG6zSqdI4jMJW1mWSxnQimVg6CqmIXrSE8dnpb04DJuvzkH0/GdStedFnucEBV18fqVXVaWsUATF/mx/eVnMenm1uep75AgiharISAj/vqkresAziNmsAp3FnXSiV9p14YXoh+j6CnGccXN/wMvv2yofIoEsmZbDXz+IOCoGSLM47I0ZTV0kWQzFttdaOF7Is7tDrELhcOx4tBtVDO3kmHRVKVsV4hwtDsINXWO13eD+K2tCqc8NmLohpqEiSRSSAsIbcsYFGE99pn6ApY9pNSp3jZe/utkhiu/MyhTkqzFv/4MP8pde/6rCx7GKLEmMHR9NU9hYstiBuL4TNyxJbRXLYOoFyLJEs2bz7/7x3+KjNw5KOeRG1UK/wCXqTqGqCttr7bKAsgyNPEcwoKMYSRK7uzTNSsblzHlq5tdqGzrb68tlM85o9LMIbZ23FkzTjDhOeOSJW0ymIaah0azawrLONGjWbSxDKx2a7hS6prLSqrJ9Zqcps9qusXc8YjRx6I1cGjWbdr1SdgiSJOVoeAJsiOKEx2/0efDaJooieufTws5PVZVyoZu1ya5udllt1Xhmt1eSnrIsY+L4KHJIo2YtIMbueJ3u+Ir/iaGqClfW2wwmDpahUbMtTEPjeDjlxn4fQ1fpD13cIOS+K2v4Ycjtw2GJDbbNk97r1AvLhHuZsC2De06xGEWLp8FRAVVSFNG3Oz2pn4VggAoijW3qrHUbtBqVUhHxoqSbZZkwVPZCJFlCVWR21toLD28YxewdDYmSDF2VL+ekIi1WDfODzlbdLs5TBBI0KqJymbo+YRgRFGqOtmVcimRStU3WOg3SLCfNBOEiSVIOBxMxGHJ8LFNHVSQMXV3QIvGDqLDZsoiTlCAURs+qqnDflbWF65oVBIkZ9t0NIlq1kwVS4LSFCYCmKcImq2qViUOWJTbXmsgINIZl6ERJwka3wdFgwnDqMhg6bKy2SLOM2wcDttfbl07keZ6zs9ZiY6XFjf3+HV/vBTFv/4MP8Dmf/BB6oV7XalTPZUCmaVawLYE8X0x++ck1rtgmnUZVaPfIokpdK9oEd4owikmKXvb8tT9taO4HQvfI1DVURaFmK4wdn9VOHUmS6I8cJKQSdbJMNmMexlq1TdbaAsbaaghyj1vo7AiQgSguTENjvz8SJgyWjuMKglDDsdA0hfHUZzz1UdWzz9HdRrNeoT928CPhDpVlGR9+cpfrOytC6iDPkaSTayAXrccsz1GYLUQnRZckSeXgf3ZOkyQFKZ+7R2XqNZO6bYmuhCyzudpE15Qznr6n4wVN4gCmqXPv1iqDiVC4m7i+SIoVUf06RogbhEUyS8gz2C/8NTdXmrRqlbt2hr8omnVRhc3kPOMk5cZenyCK8QIhANSoCYGpWwd9wjgpxbSubHQu7Xbi+iGuH5VegcIYYMpGoR6XZRm7R0NkSaJmG6W12rWtlXMXB0WRqdlGCTuM4qSEHQI0qibb26vkWY6mqdQqBjf2+iRpwu3DIRXToN2sXOq7SJLEBx+/RRDFvPTaBq16BVWT2D0akiQpFVPj4HjE/tGI7Y0WQRQvsFiz4kafuj7Hgyk5ubAdW2nRPNWG0ovB4WF/gh/ENKs23dZJwpsZBQhTiZROo7qwC9I1AVPUColSr7DZ03WN7fUOyrGCBOXWP89h7PiXTuLDictBf8r3/92/wrf805/H8fwFH9Bl4fohf/Bnj2NqGi+9d72EkJ6OWcsqTXMkWQxInYLFOGuXzZKurqlc3ewymnhkWUaja9+RKAcwmrgcDialDMBGt3EuuOD0vTfbJc7/PL8bVgqlz5kpRZ7n3Droo6kKVcvE809grIaucXWzi+eHhSlGzmgqBoKaqrLaEp6ZR8cTFFlFkbOiOEiAnKpt4gUhY2e5auhlI44TBkMHxw1pVE3qNQsviBm7PqtxIlo+qsrUC6iYoiDoNKt4gUAkpWm+VMRqfoegqgqGJnb2plFwK3Sd9VODzL2jYakVdF68IEnc9cOF1X2lXcMyNcIoQdcURhOvJNHbps5+b4QfRqy2azheRBanpEnC4fGY4cRltdWgUbOWftHLtiLmQ1UVlFzAzyRJ4upmh97RLs1ahXrVQtdUxlOPKD5RYgyimP7YWZDwnI+Z7srswiazyqoIsTov2jjNKz0KbXKxRbuoyrgIdghQq1o8dP82x8Mp/eGENEmoVUTiDxNRiauKwnDssn4OueN9H36GL/+2nyiV2aI45jNe81Le+v1/E9cPy2PeWW9z+2jIeCL04ldb9bKFlCbFNRyMqdkWQZTQadaESpwjhLNcL6RZq3DPRgfD0KjaJuG0t1S3WrgoLT/3sx6o6Cf7qIqCqesFG9bj9kGfwcRl6oZsrDSFbMFd3C+Doo+/2q7xiz/0d3jXnz7GBx67ydvf/YGlg05NkfiMT3gJjaqFH0Y8efMIXVPPJM48z9nvjdBUFdtU+K0/fIS3/NI72T0YsLPe4Vu+7vP4qi/4pPL1E8dj4gTIikSnUb1UQSFaA1Mq5sm2/rAvUFbLWiCGrlKvmDzlh7iFnMPGyonCYK3o8840WkRSV3h295goSYmihDhNaVQs9kdC4tbxwnIGpmsqrhcILoSm4YfC0WdnvUOrVqFi6tQrNrf2++i6yuZKq3Tkmh3zx8qE7A2nHI9cgiginWRESYJlGiWQcDRx8YKIwdihr8hcXe9wdaNLEMVEcVrY8V3cEZAkic2VJocDIVdhmTqrrdrCs5okKVMvuNDoHF6gJD4TkZ+FJEkFIefk593DIbkhtmyrrbpwmvdDKqZGlMjomhCxCkKhO761uqhFnaYZB/2xaFdIiEHCJQeNaSrMdv0ogkJlrVm16LZq5bZ/UGiTly7xkkSWLnlii++7dzwkSTJMXWNjpYFlGmTZtFxkgki0AI4LqnLFMoR5QlHZCH0Y6Y49/8vADnVNZaPb4LA/JkwyxkdjcnLajQphnKBrGuk5tPvbhwM+6xt+8Ixn4R+8/3H+9vf/W/7pt35lud2vVUy2V9vcu72CXtiA7R4OhKKhBBmQJjlBHFO1TbqtGo7rc9CfcNgbY1k6QRSxezTknq1uqYX9XMI0hJzo3tEQN4g5Hk64cRAj5Tmrnf+XufcOtCa/6/pf02dOr7c+dftuOhtSCMQQjAFEipCAChhQivCTEg0aNGChF0VEEVEwYBCMghQJJYQeakjZZEu2PfW208/0/vvjO2duO/c+99nduHz+IOxzT5mZM/P5fr6fz7s0CeOUie2SpAmr3RbtW8wzFvE7f/YIX/+v38nTWwPyHC6sd/nqN72O7/9HX8IDd27wH/7HYTapaai8/L5znF/tMHc8pOL3sL3w2P2Zphm/+vsf4V2/8n4ee3oHLwjKRWE0c/n673wnH3zkKt//j7+E6dxlZzQTraI447ovkB23aiss7AkPbuuFTne+dJC+UD1c7dRZ6TQwdO3QTrhRs0rjckUWbOHhxIaixeLLEQ8/fZOZaVKxdNIsYzwTRcdipzaaudQK70kDUfEGUVwSyQxdo9dpcMdmX2Cwt4boulogzFLq3WduxJ4kKXMv4NJmhyevJ9huyMxxufvcOs1C4nlvbNOomrTqFRwvFAQjTb31tc4WbTGpfP3CvH1ZSJJEkmakaYpygpw1PE9JXFvid3cw6lWLzVUBsZEQdOC56/H0tQFJllExDe65tEanIZhPqnIck7rAQ9cqBTNvMEPXtDO1XkZTp0SGAExs4c0ZxQnXtkeFg73B49d2kSWFZt0ijGK6K8eTpyDHjNBUFbOiFf3AGRc2umystNgb2+R5Tt0yRE+vSNLjqUOzbjGzfSRZAgSr8rmCXo5nLkmSIBeiTVe2RpAL8aUoTugfMXeO44TtwZTXfPm/Xmo6G0YJ7/uTh3FcnzzL97fm/SZGcc1ntocb7LeQHC+g1awWVaMqWlZhjB/GpQGC60cMpw6bq+1nrfrmBRGOH5WSClGcMpraaJpKnCZkeY7tBLzsvtYtq9g8z/nV3/8If+tt/+GQyNXTN4f8i//wf3j5A3fwnd/wJh584DLf+xP/l6dvDNhYafN1X/x6SBx2xzaNQkOkl2Wocv/Yd3z9d/4UP/uePz7R5Nf1I37sf/02f+8L/wqmrgpd7qJtZLsi8d0qsaiKIG0FUYyhqWJbrx2H0x0MSZKomPpS6dSDi2yaiWFkGKflMNYydTRFxQsjZFUmiVNW2jWmtlcmcXHvHPlcCjXGjS5rvSY3dsXwVJIK8pKpi2Judbn36u1Gq17lgTs2mcxdprbHPZdX6bXr2G5QGpOAWJS94NY68Qv48mJI3G3WTmz5LF57Y3fMzPaQkFjtndweel6S+FmEohZUedcPeeLqDoqscOelFSYzVzjEkBLGCXGc0l873k90g7D0GVxc8DhJzpTEwyg+hNMVOPJM4LYlsaU0ULnz3Aoz26NVtwqlxMPnlWUZw6nNaOrQbzdKAsRk7tJxRe998Z7BeI6qiG1VGCVsDceMbUewIFsNGjXrtqQBbhWOGxDFGVPbEcYCioRparQb1WPojIUm+7/9qV9nd2Sf+JmaqvDUzSGf89qXkqQpw6nDt/zbn+N//cafEcUpL7vvAt/4d97Ip7zsruL1Kt1mFUkSOhWmrtFv1ZjO3KISlArf0vgZV+AgfoeZ7TGcOtiOR71qICFh6kKuYDC2aVYtNFlF12Smjn+MsHE0bNfnX/7ozy9VKQzjhLf/u3fz/v/+Dv7mX335IeW/NM1496+8D7Ww0RP4dYVa9XDi+dOHnuTnfu1PTkzgi0iSlP/1G3/K3/3cTxXU++IWyTkb61EuquWPPXmTn/mVP+J9f/oIV7cGpFnGi++5wLd97efxGa96wS0/ZxFzx2NvbFO1dPIcdkfzAicveslpmtEpjLxd30eRZQYTG1lWSinkXqvG9mCKrglSnaXrh55bXVe5uNHH8QKyLLutWdStQlUVWrUKE9tD1xTqtQqbq21WCoKdqiqlHPUCmVKzbl35701skoXPb54zmtpULePYzCLLBCN6MLbFXKNeIctyoiQ7EX7/vCTx23kgkyQVUB1FxjJ1LMOgVa+iaTpV06DeNZeuvIauEYYxprHAeuZnVoWzTJ3RTGgiLCB+uiZgadmBEkHTFDZW20u3RCU5wfGYewFBGNFt1ZnMPcIoxtCEtdlar3Wo8sjJ2RnNUGSJqmVSMQ1GhevRcxluEBKEwoszDAVt/EV3nWP1gHBQFCckSbFYJhk/+54/Ov1Dc+g0xKB5b+zz2i//ToZTp0SV/MlDT/EV3/bj/Ni3fQV/5eX3EsaxgMLVKyWscwF/FDZ3gn5/VyHG/0wiyzJ+988fZXtvyt0X1xjPfUBiMnf52V/7E8ZTm7svrfGZr3kx7UaFbrN2JgMH1wv50KMnKy/+xSNXysR18FiyLKNRM3nRC+4oLAiFCp52xFD8nb/4h8cErZZFkgohp26rVujDC6PqiqEvVWB0CjkDRZFLbsV7/uAjfPnb/zNBFB+C+P3Rh5/gC7/53/ND/+Tv8JbP/7RbHksQxuyMZsUCLJ61Ba0/jGMcXxiS33W+z8ev7Yr2oqGhawayJGZltYpJs15BUWT8IEJVhBvV0R2oosjP+TOxiJVuo5QCMHT10PdUTAEdHhcOQaau0WvXieOklKxYtlsOC79QWOS/faXRg7FQRMzyHMvUUWQZ1xfzLW9yghPRc3DOANi2zdve9jYcxyGOY/7pP/2nvOxlt9ZtPhiCjZYW1a5Yd3RNRVZkIj/DAsIwoWLorHebSwdcUZyQZTm9ZpWtwVToRkiCEnzWbVanWSVOEuauuOn6nQZZMKNqmZi6h+0FhGFCRs5mvyVgVYXjyWL7GhT6DO2G0BbeHsz42BM36Xfr3HFuBdPQmDk+jZpggDbrFlPbw3FDvCDE0ATkUlUU/DwWHoKyhOMFwqlekQrRsAzL1M8MrVyEoau06hU8T/RrV9sNLFMM+3aHM0YzB9cL6LTqhduKx8w5XaNZVRVe+6Awz/0373wP47l77EYNo4S3/9C7+c0ffxu9Vr1UL1zc+LXCRmtrb0qcJJxf7ZwobHZapGnGj737fXzHj/0SM9vD0DXiJOVzXvsSQOKXf/cviJOMHDD/9BH++y+/n1/44W8iStJD5rsnhWloFLyQpSEhlazgPBdyB+OZsExLsxzHizCLY2rUrGMMzYntHtOAXxa1isHrPvk+AZktkB2yLC8dTNquz9beFE1TCsy5j6FpfPnb/zN+uBzC5gUR3/x9P8MXf+YrT0W6TOcuuyMBLXV8nzRt02nVhNOOpbOx0iJJs9LVfdUJqFlG0ZoUCI/4AFFqmf/p/6uQJOlE+YUFXLBZr5AXSXtqe4LUhoSqCqSUXww+JUmi26pRMXQcP6Ri6qW65dGFGwQcFkksflGcIGkA+amCgM9ZEv/Jn/xJXvWqV/GWt7yFp556in/0j/4Rv/ALv3Cm9yYF9XVnNCPPRTXaqlVY6YrEcse5FT76+HX2RnMqFQGDOzq8W7haLzz0dE2omuUILeDbqeRkWXgWrnREz02WZXa39s0srm0PmdkuYZTy/ht7XNjoUauYTOb7MpQFz0v8X0nC1DSyPGez3y63hnLB9gIKeFWX6dxj7no069VCo0UMQvwg4pEnt5g6Hr1Wrejlaqx2GoymNiudxqk2b0ejYhnkeU6/owE5QZigyArXd0ZC5N4NCif2kGbV5KHHr7PWbbAzOq4RvYh3fe/Xli2fn/21Pz1R63uhnNhrCzTKT/3SH/Iff+69jGcur3jRnfyTv/fXeck953n06W3e+v3/g19//0fJ85zPeNUD/N3Pegkr6+eREEiIZT3fjz5xgzd+9fcLadIi4sKa7Bd/5y9Is/xQzzWIEoIo4av/xU/wq//xrfRPgPsdjGa9wl/55Pv4nT97dOnfP/0V95fXYu74jGZOOWORgJqlo6oqjZpFu1HZl4UtJAz+2qtfyHt+/yOn+nLqmsKljT5/7VMErd7QtVPbCuOZi2VqpbSq7QW861f+6Jba8Yos8Tt/9iif9WkvXvr3heJk1dKxzA43d8fcGExBlmhUrLKS1g8sKs26RVhYHgr4YbaU2HKwwk1S0Z5M4pR61aTVqB5Dcyws5Ratm7njFQuJaF2khQbPs21N6geKtcHEpmYJ4k8QxXz0iRsEQUjNsqjVDLb2pmyutElSgTaRkFjtNg61iGzXL4akQoAvSRLmTsDcDei3azQqJu54+bE8Z0n8LW95C7q+0FhIMYyzVYZ+EHFjd8zexMb1A86vdGjWK0zmLrWqWVSpFV790rsFEqWAIS3TMZjM3XJo5gcRo7nLWrcpMNlegGnoZ8LNLmLZcCdJU4IwIc9hOJ0ThglPXNvhk194J1GclDKUwqFeZjCdM54IVttKp8n13Ql3ndeEBgkcWlwMXWO1oADf3BsLVxNZolGzuLk3YWK7WIYuML05yEpWbFdVhlPn2E19Wqy0G2zFGX4k9FlWOvUSBbNIBLWKsMiqVQy6LSH+9F3/5ZePkZ8UReabvvSN/NUDvdPTtLQX1WiaZnzeN/w7/vCDHy8RHFuDD/Brf/ARvvub3sQ//+H/jXPAYPnX/uAhfuuPPsoPvC3n7vMrrPXb3HNp7dA1DMKYN3zV95VuLMd/v5MT1tM3Bzh+eKYHXJZlfvjtX8orv+Tb8aOjzjw63/+Pv6T8by+IDpG/DF1F17RyJykG7xNsdx9J9eY3voJv/w8/L+CYR66lIsuoqswbP+VF/Pi//MpnNex+6sbeLds2ORCewkYVPVvRelAV4f1ZmTlsrrQPka4ORrtRJUkyoXcjS0vNXwbjedm2UAsIrpgryeyO52Q5ZTG3yCM5oqDrNqu4fkQYJSiyVEhH67h+DFKOoWmnaqefNdIDCy+IudvDT93k/EoX2w/wwohOo0oQxZxb6wpo8YHBKCx2MTM0TcXxxDleXO9zrvAB0DWVmwd8bI+GlN+OhUsR7373u3nnO9956N++67u+ixe/+MUMBgO+6qu+im/91m/lFa94xbH3fuADH6BSET2mPM+5vjtFUxUGUwcpF8pv670GcZLRaVhlUr5VOF7IcHZArCfLSJNU4E6DSGBg04yVTu22Ww8AQRBgmiZBFPPU9SF+lOCHSZHUYy6stalWDFo1i2ZNHHOcpDx2bY80TalZFhVTY7dwMKpVDHrNyoktnqwwCJBlidHUxQ8ThjMXy9CE7knhML/WbRT9w5iL6+1Tk/jiHBYhvELFdyy8Tm/uTUVPeyJ62RISjYrBYOJyYb3NT//qn/M/3/tBlMJQOUlTPudTX8Dbvuz1VK39h/Bf/Zdf5//8zkPHtMMB2nWL3/rRr+N9f/Y47/hP71k6vFssKMvigcur/LOvfAOuH3Jpo8vqASTN//2Dh/mOn/iNpYp+t4qKqfFdX//X+fQH7771i4u4sjXmB9/1Pv74oavkec6rX3yZt/7t13F5c78FNLN9JrZPxdJJkpQbOyN67QYbK01MXWM0Exj1iqWTZULnfqPfZDz3+JYf/iUeuzpA02SiOOU1L7nMV3zOK7i43qFVvz0nKdcP2Rs7RTtFuMK/708f50fe/funuhDpmsJv/PuvpX3Al3ZxL+V5zmDicHVnQhyntBsVKpaGqsisd5un6qLDcer9IvwwZnsoRL7iJGPq+KRJxvm1BRlODEsvrAmc+Mee2kGWJBpVUXGPZx6qIpX9bC+I2B3NuLjeQy7Er7Ik5uLmcVTQ7YR4ZmYYhooiy1zZGhFFCZquoCkqUZLQrFps9JtlXjga13YmaJpSzuw8P2SlUy9zmR/G7I7mrLQsHnzwwWPvf0aV+Jve9Cbe9KY3Hfv3xx57jLe+9a18y7d8y9IEvoj7778fKETWK3vUKgYrU4ep7ZFLsF5QuS+sd89cOQdhzNWtfS0CgXZQ8cO4XAjSNCNKUu48v3LLz1vokMRpSsXQ2L55jfvvv19sx9Sr2K6Prqpc3R4KoH6vSa9Z59Jm71Bl2O6NSLOs/LdzXniqm8yy2BlM8YKIFcfD80JacYLnB7SbdTZXW4RRQq9VvyVL7ZFHHimv/UmxMRFompX1jL3RjHajSqdRQ9cFvfrtX3uRf/ilf52PPH4dP4h44T3nWOs2RfVz4Bi+5x/1+fU//rZjdlYAL733Ai944AG+5Ud+7UT0xWmEjUee3uXSxYs4Qcjljf6hB/Gnf+OjzyiBAyRJxgvvv5f77rvnzDua+++Hz/qM15Q7j2VV52LIPXU8tvamKPqce++9mzTLOb/awRrPy14xiOHjarfJS+sV/vzTXsmVrSE7gyl3nl+h3zkbjf6k8PwQ2w3KweaDL30R//F//yGwPIlbhsZXfP5rufPOu/DDCENX6bcbPP74x7n//vuZzl2Uis0dd97FaGqXBuV3nlt5VtT30cTGz7aY2T5GVaOj1xnNXO64404hDJUKe7Nzqx2evrnHyopEtWISxTGrvQatXkyW5qx0hCPW3PUxa1PuvbQOiMXjIx+99fNwlrjs+uwMZ2JnXWkhS/D01qggO4WcP3+eT3rg8omwTaO+h6YoB6QmAs6tdspiMwhjqttDnNHW0vc/Z+2UJ554gm/8xm/kh37oh7jvvuP6xQcjTYWcpixLVEwNLwhpNSr4UczM9kjSjLVe87ZaH6YhSDS7ozl5LloBjarJzcG0fI0sS6du8xeRZYLsEyUJqiKz6waM50LYSlFk7rqwKtAHacr59S5JkrDSbnB+rXOs995t1bi+MyJNs5LKXzFvbyfQadVwtodUDI04Ftuxey+tiT5hltOuV5+zSX2vXS9lB+6+sIqmKmVC6zZrZX/ypfdfFPrf5oKqngsUTc1CK+jf51baPHpl+9h3/PFDT/Frf/AQc8d/RseYA34gdKVbjcPnfX6tQ8XUz2TXdjA0VeFVL7mTte7xYfnRSJKUwURonVdMYynF+mAsYHwLETI996mYQkphYnvouorvR1iKuN/z/DBF+9JGj0sbvds6n5OiYhmHdn+dZo2f/q6v5cu/9ccK6YP9BbBi6rztKz6bL/8bnyKSoC7UBKN4Ura4/DBGU2UUWWKlI6CwVdN4Vgk8TTP2Cv0kVZEJY8EqtkyN3dGMZkEoOrfWxQ8j8lxU3AsI8XBkk+YZfiCMWnrtOrIs0Sgkhxfidqbx3KS/elXYSmZZTqdR4QMfu8K51bYQt2vXiiHz8aIgLtqviiQxsT1qlkGaCUKgdaBfvhD7ck6Q5XnOkvgP/uAPEkUR3/md3wlArVbjR3/0R5e+9umbe2SFlVOzZmEiRIF6rRovuHOzwJAK/7t69ez46EatQr1qked5Sb9dCCOpqhBG6h0ZiNquj+cL4aRmTUCbojjFD/dJKZqq8LQbljdAs17hlS+6k9FU6L20GpUT2aAVy+DiRl9A5grp09uVOtU1lYvrPbwgYr0v2JzPdDCT53nhDn6ySNdJWHqtcEuPC3VJpP3Kc9EXXDRPHruyzdWd5XedH8b88Lt+gze+5kWl/srRWJCFlsU9F1eRZHjpvZdo1g+jCL74M1/JP/m3//Ok08fUVd70xlfyc7/2x8LZJ4qF6Nb5Ff7p3/+cAvVzsjXYQtMmThIMTWPmeMRpeszCbtn5GLq29Jr3W3VuhOMSSdWqV85k0PxcxRs+5QX8zn97O7/9Z4+wPZhi6Tove+AS914UBJfx3C2HsnquMpo5BE5AmmaYuloYoAgEhdCueXaYbS8Q8NKNfovh1CHNBU76vsvr1Cwh4WAWGv6264MEK906eyObuSvkGi5u9Lm0YTGeucxcn/sur6OrKntju+yJd8/Ayl3AbBVFvqWWkKII5vli1tGqV2lUTYICMacoh81Vrm0PywF7lgkdGMvUaS6BVK50G9y4uvy7n7MkflLCXhaKrFApaNijqdAbWek00Av7sWs7I2HbVtB3L6x1z5y0Dg4ZlEIZcDR1iJOUXqtKxdQLzKZ2aKCQuhmOG3Ku6LkddFxalkyOVjSnhWmcjSl6WmiaSvNZGqrmec7OcCqYsJJExdBY77fPvKiEkZAcSLOcPMvJ8wwviDA0lTAWTL8FVG5rb4quKpxUa1/fGfM1b/50/v3P/CZhHB9Ci1imzme88n5+648fPtZuUWX499/6ZbzmZfccu9HzPOeHfvrXl2owSxJ82ifdyw++7W9hGhpv+4rP4tf/4CEmtkevWeW+O9Zp1ytEiWh9LDOXBtEDXXhsglhQHT9c6jZ0NGoVU+iwhzFBGBOnKWu9ZrlzOQqv/X8Vlqlz98U1us0ac8cniGPWus0y0ThBRFborA9Gc2wvJPIcbuyOWe00cFyfqzcH5MAd53pLTa/zPGc6dxnPPBRFJGj9tPPMYb3fAklCUVSUQvFvpds8dH0qpoGuKoRRQrtRoWoJS7OKZQjJjm6DasXA0DSxSyieWVVVeHQ2OPW6jGcOj13ZJsuhaulcXOveEgGmqUp5/ge9b4/eG3vjOUEYl4WjLEloqkrnhM8/rb33vJB9dK3wFgyiom2RUjF11rpNbDdAV9USGG+7QprymbYLNE1lrd8ijhOu746F6z3QqgmrpGoBDZJ0ibkrBoaWKZx9bC9ElSWmtofrC6Gi+jP0zvzLEK4fITtBSTt3/ZDJ3D2z4tvW3gRFlrEMUZHbXoipC6XHqmXQbzfKm+2eS2tLTYJBtLVefM95VjoNfucn387f/db/zMev7qAVyotv+bxP5Tv+vy/k9/7iMf72P/lR/IM9bkliezBdev3f9Svv54ff9ZuHrPNAIBu+4W+/ge8pdF22BlMcL+DzXv9JhHHCcOJwfrVdFgozx2MyE1ZzRxPq4vwOMvaGozmaIlOvWscMEg6GpqlcWOuyu32DWsVgOHW4uj3i7kJ61zSev3uqWhhH39ibEIQRfhCTpqkQDXMDpjMX3w+pVkwub/YZDUPCKOHq9pBGoSuUZWLmtLArPBiD8ZyPX9sljhPSNOXGzphXvOiOpYncMoS5ArmwPxtMbdZ7Tc4fcHNahKLInF/rYrt+kWwNpnMXxw9KKOViCPrk9T2hkGrprB8gtS2LKIr5i0euYqgKmqYytz2u5NyyM7BQ1NwbzfDDWMz7OvstujzP2RvNubY1Em5AbsB6Sfi7bYwJ8DwlcS+IqFg6W4PCYqpeEb3nsXgYDuthP7vvSpIU2/W5vjtGlZWyhzq1Peaez95IVIEVSy9xqgv/P+EcFDL3AjqtCrWKwdwNUGSltHN6LuIgnrVi6s9I+2HxGUmaUbGMpQicOE3RDlQEpznRHA3RRsn2Bb9k0QftthpLZxebK20+41UP8N4/+tix7zB1jX/8FmHP9sCdm/zZz/1Lnrqxx3jmcveFVVw/ZGs45Ud+5jeJjrRakjTnH3zHT3Hf5Q0+6YFLh/72XT/+y0t74Uma8RP/5/f5zm98E4ois95rMplroqKuWkjsw0mTNGVnOC/V6BZ8hcU9KYyOq0UxkLO1N6VVF96Lo5lDnCQnqimCSOQfvzrgK/7Vz7EzmiMXOiTf+9Yv5ks/51NO/xE+geEHEVt7UyqGRhCEXNka0m/VSJKMSxs9oe1drxBHCZ1WjdFQOE1N7ZC1bqNYuBTiRLTbDiZbocY4I02z0mh4MLXZGkyPafrDvurkZOZiWTp3nF85FQigqsqhCrnXrhPGiTD5RojUjecC2aUqwoVrZyTYjwuT6zCKqVlmOd+YO75AlRUtF1mWmNseaZahcnpXQNfU0nVq2XWe2i7r/RY39sZEccr2YEq3VaVRa536uSee/zN617OMimUwtz2iJOHyRk8MfAph9XrVZDR1BemncPZ4pv3BheZ3nKSMp26pe2KZOkma4nsBaQ6moTOeuTRq2T4RRxZ0X0mScPyAeVFdVQz9kOBNGAlhLEWWb5l88zzHdn3mjkAHdFs1VEUWiolhhOsLAf3za10urIsW0qKHDRwaMh6MxSA2iGJURWY8cw9ZTi1CLwgTiyoyjFOaS7a+y0KY0QqRKlPXCiamVLZPwki0CFRVoWLqSJLEf/uOr+LzvuGH+NCj18iyHFUVc4of+WdfdiwB33FuhTvOFXKqbkAQxvz+XzzOMpBKGMV8/3/7Vf7H933doX9/+ubwxOP3g6h0CTqq9JjnOcOpjWXobA+mhaJjDUWWmNj7fIVF9DuN0lUnitJS27xeUbDdkNVudmI1/qcPPck3/MBhzRXXD/n67/wpdFXhzZ/5yqXvOxiL1oQXxuiqIo5Vkfc1uzn5XjkpgjBCUYQHqe0G5HnOeOZgmhqqKowJeu0qXizYw3Ei0CHtRqVY8OTSe/WovIVUqHEenO3pinqi2QoUBi1LGNlnCbEIdIiTlIXhtxtEZWUuWmABWZYLbHlOybwczx1URWFmuwRBjB9EhZ5RWjreP5sQhhLiWdrst5nYroBKrvcEua/ov9+WdPazOqJnGJsrbRpVE8cPefzaDtWKQZ5Br12j3ahhGjpz10dGotOqPeNJtxeERfVo0ikMgMdzl7Vi265pGlKa4nkBvXYDUz8+dFqYBCwiTtJSlc92fa5vjwu9cmg3aqdW6DPbY3c0x9DVst/Ybzfwo6ig6QvVuae3BiiKzGahcrjwOKxVTNZ7x1s5QRjjhWHpgqKpCsPpcb2VqiWQFOOZS5ymZGnGZO4SxSm9dv2WPd31fosbOyPmrlBb3FxpoaoKjhdwc2+ChLhJ61WLjb7QRPmdn/xW/uLhK/zJQ0/SqFX4G3/lpUt7pmmhSBcnGaoi8dSNvdLY4mhkWc6HH7t27N87zeohlubBkGTpRBp3tyUStutHaIrMpfUuSpFxlMKO62hUiuHy1PYOHJdYcU57AN/xI8tFs/wg4u3/7t286Y2vOPb+hcs7iF3MaCruY7NwS/KDmI2VJoOJzdwV90q9YrK25F45KRRFJs0KVvJaR7QaKxZBGPD0zSHdZpU4FSYuUSxYxOfXumiqwo3dcVn1dlu1pTuzjZUWH3rkKhISuSQklduNT4z2CSyKDnHuC8GqRfEShBFxnHJjd0IlUFjvN1EUgW3/+NU9Hrhzg167zmjiMp07+JGOIim85O4Lt7yeYRQX3r3y0uugayo5guhmGhr11KJVr5CkKTeuC7LSSX60J8XzksSvbg8YjuZi5Zu5hFFMp1Ejz8XN+1zpJix+OIBOq0oURzheSNhIqFcMdoczWo0qqqwwd33qldaxB6hi6rTqFZ58MsQuXKn77TppmvLQx68RpTmqLNNtVpnMXZp168TB1NT2qJj6ATywsJTLshzbC6gVZA+JvHD6cbC9fZMF2wuY2drS4crRFtRJ/bVeu067UeHa9pg0E8JLc9cnThI2VztEccJo5pClOY2aeQh1E4RRWcmbula2nwbjeblVBTHHCBpxeRN/0gOXjlXei0jTjO2hwMFTQEOTJKPXrp9I2wdKE9qD8bVv/nR+4L8dJw8ZmsqXfOYrT5RekCSpsEcrzLFnDiYLwar8xPfpmioU7+YuiiKTZTmrB1ovy+L9H3r8xL/tDGdc2xqy1t+Xwj2kbY+EKguVxGaxS9Q1cb0HE6fw31zcKyGW7R26V7IsYzL3CKIYyxCmxwc1a6pWUCJkLm32URWZq1tRcc9UadYEsmq12yB2G+XO8+JG75BO9rJY67V4yX2i8lWLAeWtdO+fqzANjW5RxOVZxt54TprCU1sjjEmM4wds9lpCNVMVeH0JiTsurOD7EStdAZ1clpTzPMcrBr9RnDAs7OnyPKNZqwj4aCh2r+1G5ZBLVRrG1KsGjZrJ1e0RFUPkhiCM2R3NTmzJHI3nJYm/9/0Po2uif/bie87hBiEXNnqEUVJqHzwXUTENVFWoocmyTL1a4e6LazRqFZ68vsfGSouZ44vWQhQvFb2RJInVXpPNlSYX1rol5X9vNMP2hNvQYjverFWO6SAf/ayFDx8IvLOpa+iaQhBFqIpElAjjZiRR9R/UXtdVdWkVZ+gauip6faqi4PgBvVa9hEQejbSgSS8e+IqpM5g6aKoq2gqmjlwMEEFAN4Mw5srNITkZqqwycwJ0zWGl2yDNcnTtwPcU53mWGIzn+EFErRis2W5AqyEm9pc2ezx2ZefYe6qWwT/822849G9PXNvlDa9+Ib/1Jw/z4ceul5ojtYrBxfUe3/+PvuTY5yRJynjmEiUJVVOn1aiW4mejqUuSpLfkK6x0G9SqJknBDj7ptcJMYIaqKIQsX5yyLGM0cwsDhBaNWoW54x1Cw7h+wMwOaB6EtEqil3/wXtFU+dBgedGXdr0QXRP3ShjFZf9elmU2V9rHjLtNXWj+LBayBbLpYBysek+L9X6b1QKHf5Jx8nTuMnOERG2vXb8lVyRJhOKfXLRNZFlaWkT12kJsbWZ7hFHK3POxdI0gyXjiyg5bOxPOr3Wo10ykEpqWs9JtnNjWWaC9RA6Ba9tjLq51S1nhJ67vCrtHy8ApjC02+q2ySF3sDDw/LCCKC41yDdsLToW6HoznJYl3mlUqls7HnrjJ9nBKq1ElTVOQKLexz0UsBiTTuRhI9Asqq5gOD5EkiUbNpFaxSDIxyMoyAZtb3MgH/QsP3lBBGNNuWLhehGVqpQPHsp5ZFCeEhcfkaDInVhXSLMfSdepVk4qpkyYpO6MZjZqFJMk0qkKGdns4xSiwxWGcLN2CLib0k7nL3nhOFCfMHR8/jDi3epyAVN6iRaIdTObsDOfEUcJk7nBps0+tYiJJEtO5J5KJ67E9nNKoWuR5VPa4V7oNmjWL0cylaurEqVBYNM64FfTC6JDuuySJJL3abfLzP/QNvPYt34Xnh2V1begqX/JZr+RzXycUMj/46FXe8s9+nCs3h2iq8J38rE99MbomrvEXfMbL+dzXvfRYhbjAe0exMMPdHdskaUa/0xDoCMVDVYXSpKGrJ0LLJEk6k4zDcGJjuyGf9+mfxM++54+W2ra97P5LrPWapUVa1TJJ0qw0CgFQFZVmw8TxBPoiyYS3ZNUUmjoLi0KB196/X+MkxfWDcoe7QGtICCEyteihH02a/U6Da8VcKctPlrg9a5zWjlh4fVZNQXq5vjvm0kbvxJ3QZOawN3FIk5Tx3KHdFLvqVv3wMHoRuqYKVVQZnr4+4IOPbXHu3JpY8JKEKE1Jkoybu2OajSqacrpLVhDGzJ1AkIjyHF1VGReaT0ma4foRG/12+b22d9hecXF8qqqQFzLFsiwMOlRZLnko9apVIvqWxfNjClExsf2QXkv0MOvVCnGSsn4bfaCzhq6ph/rUw4nN1PFoNSwefnKbq9uiH3znuVUMTeHm7gQvjBG0FWEYbBoiSe8MpkRxQrViCk/EaoVQj/GCCFmWuLjZP7aL8PyQ67tj0iRDksUq26xZKLJSyoXKssw9lzcKBppgwC2m8VGclGzRdr1yItRSVRXqVUFuWOtWSkW13eHsmJSrpqliezm1ieKEncGMixs9FFXGDUOGE7tkoGnFzWM7ImkYxdxgPPPEwgsFI05ogeuaykqncQiGtUAA+AUqaaW9/3fT0PD9iDTPmM5cvCAufUHvurDKb/znt/GL7/sL/vgjT9KomLzqgXX+4Vs+H0mSuLY94q/+ve8tZwYLwb/3/MFH+Jo3fTrf+9YvPvG+iGKheXOQ0DWZe7QbVfbGdvnb5HnO3uT2SGfLQsiQanzzl7+R9/z+B5k5+1ZrsiSw2t/5jV8k/luWyfKcLMuoWgbjmVsMKoUQ1bmVDooiSGy6ppT3ShglzFyBzG/XK4dmDxKUu0Tb9dkdzoiznGmx2F9Y7y3dAVumfkuJ2+cq5m5QthsF6S4pzvF4TvCDqPidDHbHAk0UhgntXpWp7R0bRh88n8HYYWc4Ic+EeuquE/DgA5do1StcWOswsQPWu00qlnFqVyDL88J1SyiSNmoGw4lLlmWEUYwiS4cW4JNC11RWu82CiAR5lpPlGVPbRZZk9iZzVPkvWRJvF47qruvzkns2ue/yBrqmHEvgi35TFMXo+q3NR88Sc9fHUFUGQczlzRVmjke/UwNybDfAC+PSSiqMEoZTuzQ0NRsdFFlhbzSnUROVsnAg12nVKrSXtGNu7o2ZzF3iOEUpXrvWPa7YBssJRP1OoyQA3KrNlKQp0gFTaENTT9Qm6bXFrmTu+pALadXF7mBme7h+QI7EWrGVNAsmmesLCJ8kQa9VL/5/oZm8rGpZ4LKTJBVOOn7IVjzl/Log0/RbdR6f73Jte4ShCQH+4cQWrTBFTPG/8gtey1d+wWsB+MhHHy6rmX/33399KdvTCyJ+9H++j2/9qr9xOr/gCKGrnCUcY6Jy5vbQSWFoClGS0qpX+Pav/iz+4MPX+N0PfJwky3jDq17AV33hX+H8Wpc8zwUlXBeoEGEA3WQ4dchzWO3se8UefR5We/t95qMLjrbo39suO8MpeQ4r7TqNquBD+GF04hzqVhK3z1UoikwcpywOPT/FUzZJ05LYF8cpNcsolRYl6biK5kILyQ9CvDAUJMDxiF6zhqGpBfJERVVVLFPDWELQORqGpiJLwiRdUxRM3WBjRSOMxb1+cb3HcOZQNXVyoF2vnlikthrCTUtYBHqMZm4pzzGaOQIBdMJxPC9JfDgRYumf/OI7ufvC6okr+0JIX1GEYehZRJ5uFbqmlqJMFUsjy01kSebazoi5G5CkCVVTmPIqikSWCqOKMEqY2z5+lEAOru/zsvsvFT5/J+uV745spAKrmmYZg6nN5Sg+ky7MQqPDD2NMQztUwZ50boskYLsBM8ej26qe2FurWIYwlM5zpnOPetWkXjPpNKr0WsJEY/HwtusVHC+gUTNJEsHeO0k4/2DEhTNQrUg4FXOf4bhIUs2axeXNPqahlTIJc8ej26pDLpXbzKMiU7/x/o+eKCeqqQp/+tGneMOrX7j074auHiJ0JWla7iBMTej5WIawyjM0dWmbbCFBcNrvv4iVbpMnru0Kvfss55/+/c/lB95WI8+FXsnF9S67oxluEFIxhHuMH0QohbPNWU2+T7s/hD6/huuFWJZ2qK/+bOzvnm3EcVIwqusCEuyJ1k3NOrl1o6lqCVywDJ2d8ZR+q0GaZgXEcv/3EAbdQgtJ1xQBKdQ0XnjnBka1ydj2iOOElY4wQj/p9z4ai3btoLBe63fqdJqiQN0aTElzMayfOT6XN3v0jujUL2YAC1PpfrtOpTCIPqyTngny0wlyQM9LEn/9K4Ry2KIPtyziOBH9pYpRsplGM5d2o/qsBp/9dh0/CAmiok9t6kxsh0bVYrXb4LGrO+yNbHqdGp4fsdZrIkkwtX3a3ZSqKeREx3OPKE5vmYwVWSbJMtI0I0lzZInS8eW0yPOc7cFU0Nl17VgFuyx0TWWt2+BDj14niCI0VWUyd3nkqZtc2ugfQ6wMxvNyqz6a2kRJwka/Ta9dO7awViyDC2tdpsUN16xbZzLaOOhuszA+ID+cNDRVRVWEr2maZmwNJpiGju2F1CrifxfRa+3//qfJFC9IXp4fLsXv265PEiekSUKtVqHVbJWV7caK2Hn5QYRpCOONo9c8SVJu7k3KnUCnUT2kMHh04QxC8UBrqkIu7ZPYxPBaLGYLNML2YMoffugJAJpVi7surp5Ix76dEDOgCndfXGV7MCMME9Isw9CUspd+1jgI2TsYcZwwmNpEUVJAe09msD55fY+3ft/P8Jt/9FFy4K7zq3zHN/xNXv+KB1iYMS+D06ZZhq4qrHTqDCYOuibTrleEpVqcsNFvHZK5iJMUP4rK4fA9l1b5yGPXGIxttAAurHVZ7TSZOT69dn3p7704Zy+IDg2xDV07Zs/oeALl06haNKoWQRQTxumxz5zZHluDKdXCo/P6zoiLG2IeNZo5zGyP0cxhdzijUbfonJBqnh+3+1s8/FmWlUL4ixNfbGufKTV1EYaucXlzhV5LCOZMHYHK2Fhpk2UZNdNgZzijYumsdhvldlxRZKIkIQ9Er7LbrC71yDsa6/2mgFHGCZoisdptnklHJUlSvDA6gCAR5JK4uIFOPj9x3G4QMBy73NgZ4wU3mN8XEDpO+eAdXSRbddEq6baOJ/BFWObtmWqAWKj7rRp7hX1VngsY3sGFuNWoYHt+AZUTA8bVbhMJAZXbXGkjSxKqqvCkPy3f9/f/5ut4+MmfWcrSrFctXnb/JQYTm4tHkrjt+kLoqWDHjm0XwxBOMJahoRUwsNNiOLGJC4SPIMa4pTDZzmBWkqLW+k0xOC1acM26xc1eg5uDiXC70UQ1d/DYPvrxazQKFrPrR1y5OaBWzGGeTSyQHPWqsP1zvBBVlUv9jrPGZOZwbWeKVt2l26rRLsxI0jQ7RJ45jcF6Y3fMq//Ov2Lu+qUN3WNXtvnyb/1xfuJf/T2+8A2ffOw9w4nNqLA8y3M4v9rmzvMrx5x6jiZLiX1BNUmSqFkmF9d73LixzeVzK9QrBqamY+oaG/2WEEYrRKs0dV8idm80Z2K7KLKAk673m0t3SEeJTeoSbfw8z3nyxh52oZDaqlkYhoofhLSbNTZXOjz0+A0MTeMl915gdzyHbHkp/rwk8ZNi4UU4mXtFWyBCQgy/gjChWjFKZtqz2f4taLrtZo2Z7bI9mDKa2jz29A5JmrGx0ip73YvvWe82aDeqqKqMoQnG4gIXfVqs91ukWUaSZCCJncBZ+ouyLLCqi1bC4pzl4mZcCC4du2ELJTnHi9gdTvBCocJ2c3eMnvvFcNEo3dAPLpI5+amLZJ4Lw4Icwf486xC63axhmUZ5zY4uYnqhKeKHEX4Y0Wl2y4HQ4viWVdNf+jc+hZ/8P7/HRx6/gX8gkZuGxr/9lr+NpshLzQ4E4kTg2meex43dCVdvDtA0jU6zwqWNPr12/dR7LIhiDE0rj1GWhY7K7kjoStcqBkEUc3N3zLnVDnm+f707zSqtTkuwamuHB6aeHyIrygFUlIYfxmfy2zwtxjNH+EBKAjm0udJmpXv7Mybb9dkdC8JaxdQZjO3SzDgq2iKL3nq9Yp7IYP2+n/i/uH547Lz8wtPzCz7jwUPvCaO4cOcRRUeSpmwPZ9x5fuWWC5CmCWmP8cwt5agtw6BRM9jstwChK+R4gsU5tcW1Iqf0zASYOl5JqFsgiOpV69h9YhoaORSkHwkviFnpHG4DT+curhdi6CqGpjKxXaqxcUgKuVW3ymu53m8x2V1OZPtLlcRntieqw+KBTbNMJMoc2o0KlqHz1I1BiXI4v9p51miWRq3CznDGE1d3kMiL7Z94IKe2V1aenWaVRr1CGCckWUavvVwz5GgYusaljX6JYT1rNaUoMv324Qq236mTpBnXdkYkaYamyGystA8tCrqmUq+aPHZlm8nco1Ix6XfaxHFCFIblgE470PvVNeHsXauYJ/ZUkyTlxu4Yz49QNQUJOL/WPbM6o3jdya/VNLVEzQiYYkHjzg7TuD0/5OkbQsq43ajwmz/+LfzXn/89fvx//Q5Tx+Ol917k73/ha7m82Wd3bHPuQEUdRsIPcWt3UlzfupBV9SMqFZ1eq4YXhAzG9on6M4uomIZAQVSMkhQkyzJxmlIrtsemruH4AY4fMpzOuboV0m5U8IKICzWTduOw5GgcJyAJRMOC7h1EEdXCUPiZhh9EDAokhyQJv9a9yZyNUzReTgrPjwqYnliQNE3B9aPCR1M6xJPICmeqZYvhL77vL06cZ9huwOPXdksDB/FZ+aGhvaooBGFyIhfiaCzIY+Opg2nqWJaMVtz3uqYQR6JITNL00LUKo4Td4VxAFg98nlxU48sKSkPXOL/aYXc0I06Exngcpzx+dQdZluk0Kjx1Yw8/jBmMbdrNClmWUz9AclQUubSaW0iSnBR/aZK4YJO5QuIU0Us1dY161aTfaRDHCU/dGOD4Pp4fszOcsjee8+D9l05M5Gep2CVJOIJcPreK6wc0ahZhlAg/zwOvUxWZC+tdMcji1i2hg7GATC3C88OS0NAqWFzLYlHBxkmCWlRnT9/cQ1NVTF0jjBK29iZc2uwfqqgvrHe4ui0WO0WVoegvzkNK/LYkCV/D4cQmiIQk5kmYWM8PeeL6Llt7U+oVs2iHKAzG82fkRH9arPaaXN8Z4XjCqPkgjTsIY3bHDv11GU2S2BvNWes1+bov+Qy+6oteh4QoBD746FVu7o1p1oS3YVqwTK/vjErdlCeu7+IHEW4QIityoeEsISEL8swt/Be7LbHYOH4IOax06kIRk8OD2DhK2R3OWO+3mds+o5mAWp5b7RxKPotWQZpmhc55gu2H1EyDB+7YeFZzoINIDhBD3eAE1NKtQtcUZs5+ayBN912rDF2wQCdzt0zo6/3m0mfwtOdy2XOrF0gQL4jI8gzPD+k0Tm79HY25I4yIm40KaZoznTtUdBVZkgqnI4k7N/ukRxYLQ1dx/ECg54qhu6YJb4JG7TjccnHsFcvg8jnhICZ+W5eapZNmGR969DqmoVI1daqWSRhFtBtVLm/2ys/TSxiwg3QLM5vnJYnPbI/BxCbPc9qNKq16ha29KbvjObO5R69dY7XXIi5MHWAxnIhx/ahYsQxGM4e9iX2sf7kwTR2M52KCvNplpejD5nkuflA/RFMUOs0qpq5Rq4pkGYTiO9rN6jH0xVlQCLcK1w957OktPF8Qihr1Cg/csXHionBQizyMYqF0WFwTcYPtIz0WoSgKL733Iookl8QlXVXoWId3LqqqsFZsJ0+KLBODRk0VuHZdV9kdzTm32iY+w0zgdkPX1BN3Ln6RcBdtLMvUmdk+jicMaT0/4sr2kJplYOo6zZoYKgmTAZksh0qxa7n34hrjuUe7WcV2gtIoQ9NkZIlbVr5C26Zdar4sHr7VboPt4ax8XadVxfZCDE2l36nTa9f4qDc79Dt4fshwalMvCFaqIqEpaoEm2R/u3dgd8/Pv/XNsN+BTP+keXvvgvScmwyzLyHNxnKqiMJ45TGYOmiYgdK0j0Es/iBjP3fKZPGkX0qhVsN0Q1xcyFJauH/qsgwxWQz9ZR/+L/ton85/+5/uWimC1m1XuvrB67HqvdZt88NFr+GFMxdAEGSpJz4Tfn9keZiENoSpQrZhUDJ0oTsiBC+td6jWr3B0s2pVeEFK1zFJidji1CyJVjc4BU4k0zdgZCUbsQilz0QJ0vICKoRULqUycJqzW62WuSVMhM3HU4OSgy9bM9j7xbve3EzvDGYamIEkyw6ktZB+zjM1+G1UW4k1ZlrOx2ikJC7IskyRJiXaI4hRT149VTHGccG17xHjqFDZeEU9vDUjzjM2VNpO5y2Bso2sKW5MRjz0tNENUWUhtCn/DBndfXF16Ax6sEpJCO1mW5TO3FW7sjJnafinpujOcstJpCAH8W4RS9MkXN1iSpqXfYBgnBa5aHEerUeXF914oF8tuq8bOzeOiUSdFFCfiXBEY6YppMEQ85FkutMTXTqAj267PYGKTZSIhtBsV/DA+kZq+6PEvqMdHdy6LOAgzBHH9w4JxWbUMBmObMIhp1ypUDI3hzKFTF1tVRT48FJdkIcB0brUjNEt2RsRpSrdWZ7XbPLMc8NHjbNQqmIZeJpcsy5m7+xTqKE6PEUAWgkmL+8oyBBy1egC3/f0/8av86x/7RXJyojilauncdX6VX//PbzvGT1gIrSEJSQVFEtfT8yOcICQINe7Y3PeZDcKYazsjdFVFksQ9en6ts/QaCIGsNnu7NfqtesGSTcu/HWWw+kGE7QZIkuAiLBblf/yWz+Zd//f9jKfuoU6Brir8yLd+2dLFKYgS1vvNEj/t+iFT2zsT7NiPYiYzV8gLF1ZuQRxz/2oHTZVxg7hQaqyz0W+xM5yRhjlVU6CTgAKzv7wFtTee4fmh0P5JU27sTUq2qaaphGEszB+K+1CRFVa7FcIoxg8FHX/ZOS/yymA8P/HcnvMk/uSTT/LmN7+Z97///RjG8gdhMneIi2mtqavIkkK7KSbk6/0mtapBxTAOnZhpaKx2m3zsiZtkuaDnNw84dSwiSlLSNCXJM2qWUcKC3KJiHc9cLEPlys0x13eHgIRhaDSrVqmNYixRM0wLmvZipe00q4ymNlkOYRhjGTqrPSFRetoWz/XF+xeVnioreH544usPhqoKxbXtgaj0JEkM0K5uj8rp+2qnUe4gmkcYnrtbx9XxhhOHueujyBJrPQHNGoxtJrYrdh7FceZ5zma/xfZgWuq7dJbYWwVhzNbeFMvUkFWJwWTOcDIHpGKoJMxrFzR2xwt48voee+M5qqJwebPLuVUhwxvHCaOpU7JkmzUTUxf0ZUmSBHmq0PgokIu0GxVsx6di6kRRQoao2Bc7CdsLBHsRypnK+fVu4ZUqbPkmM6Hf0apXzoSFPxoLmvUiOo0q47lbDKYFTPLo6xctnwXT9iCG+7f++GN854//0iFik+OFPPzUTb7yHf+FX/h333jo+gt0lUaeS9iuz3TmcmGjV1oiekFYor9ALLpqITUMYtGeOf7SJB5GMVe3R3zk49sMXblYqAXNfXOlc6iY8YOIa9tDNFUlyzNmjsfF9Z4wauk1+d2f/Gd88/e9i9/+k4dJspx7Lq7xNW9+HZ/1qS8Wu86iVbOotBetpkUoS1AfizhYbNmuTxgmRR4QCKjVTkMMFYtzrpoak7lb4tIvrncLrsjZ2jWOF5bvFTvFpESS9dsC/257AeRw5/lV0iwTrTiEquut2rO6fvLfn9Mk7jgO3/u934uunz7wm9o+qwUVfjB16LdE4/+g3ka7WTmWSM+vdTF1je3BFFWVhRP7kUQiFyiLLM0IwoQcAROSkMq+4Gjmsjee0WnW8IKIme1jGSLhn1RRj2ceZj0qV9qPPHaN9X4bVZHZsz22BxNh+Fyvcm6tfWIi77ZqjGY2QaiQ5Tm5BLVT8M5Ho161sAzRW5OQuLI1pFpsubMsY29sn9nHczhxGM8d6hWh9XB9Z0y/U2cyd0sMthdEmJpGkmUkWcb6SouNfvvE6+QHIYoilS0PVVG4uTfm7gtrgFg4BhMhk5umGde2Rkwdj3ZdSC/c2B2jaRrrvSY3dscEYczM9bGdgLVek16rVqIFDE1lWmBpaxUDXVEIJJnNtTauH2PoKnds9suEutFvlYpzYrE+fA5JmrE1mIqFQZbZHc2Fwe4ZiTYnRb8wEF4oIn7cnRz6u2UKWOhg4ojq2dAOzSe+/7/96lIYZRSnvPePH2ZnOCt3RXGSkBXuMX6UkKYZs7nLubVucU8IeaeD9+eC5r+Io8PkRSwghIPxHNNQmcw96sXzkCSi7Xb5wHxmPHfRNa1MlK4f4nhBuYBf2uzxTV/2Rr79H3weqqoQRilhGLM1mOJ6oehNA+sHFAbDMC6PLY5T6t3Dz47t+uyO5mSZ8O/td+rMbL+EdwZhhBdGdFtVrl/fzy9xkgo/z2KeIUucKEWwLHRN6KMbhe3kwWu4aBGGUYwkSULGIxGa7KqqnKlF22/V2T5hI/2cJfE8z3nHO97BW9/6Vr7u677u1NdahqimZEnC0jX6bbF9HU0dQFCLT3Ly6Hca9Np1FmbIxz7b1KlXLB5+Youp7aFpCndfWKXXrpWIjys3B8iyhO0F1CsmhqESJ/GpP5jrR4dW2ihJkSTh8K7rKshCPMiPhJbKSRTmjX4L1w+EMYQscWGtQ/c2iRyqqqCiCKlWaf86yLIMUl76IS4iz3Nmtsfe2KY/sWk3qnhByEOPX0dTVcIwoduukZPjesEhZ25dE+igy5v9E2GNB2OhS72IOE5Qlf3bTJYXU3fx4MSpMNRYtFBcL8PxfIJQtGBmjoeEgOXtDKZouceLD7m1V4niBNsLqFYMTEPFNAzajRrrByRd4dZiVX4Qosj7RhemoQmBo2eZxOHWvpntZo1GrSK22keu8aNPbZ/4PlNXubo1LJO4IsuMCihh1dLxfNGPH88dXD/iY0/cZKPf4q4DPedG1WRquzjFDkeSKB2wDsaiOpYQ2GvLUEUrJRfmLUmaHTMEPi10TaXXquF4IVGSFi1Ni+HEpl9APF0/5M8ffoqKYRSWazJrSktguldaVCyDIIxFtR0nTOdeKZk7dbxDz4GmKmiqBUhULZOaVfgWSDKuH1I19RJCGERxIblxNgTP2mIg7wsT8W6rdqjQUZTDpjELNNbBOKobf+jYT0n0zyiJv/vd7+ad73znoX/b2Njgsz/7s7nvvvtu+f7B7g6NmkVOThynbOWC8r3oWe54E3a2bv+4siQliSK2Rg41XUZvKuRpzt7eNg09YXdLJo0TovkE3w1w3YDAMrgeClF9I/cPJbBDn50lPPzoY4Jxl+fs7k7xZiP8OIYMkiwjtBtEcYo9rlG1Tt6NpGmGmgqZ0MgNefzx6e2fLIUuyXBGlolkG8aJ6JG7k0NJYDx3mTsBWZrw5x/8CHkujsEOQmQkriUpjZqJIsu06xbToh0hZDIjmnUT9xamsvvXKWdvYuP4IXmWoykKyPCR6aA0tK2YGpk/IU5SrmyNmNsBFUsnSYVHY+DWcSYDHr++h+PHNKtmCfeqGjIf+9jDx36npBgwarJM4rmkksRT85OdfpaF7QYMC9IOUGiYqNiT3UOvW0gmy7KEVQysbieCIOCRRx458e95LoSpbDckThIqxsmDOz+M8O0RjzyyX6lvb2/hhzGyLNp2qizzLT/1K/z+B5/C0FRB+KmYfP83fi4vvWcTENdvwQGwDI0nD+wW4iQtBMxiRkVf2fEDBtPrZLmE3axgz8boqkzijsvr4YcxOyPRJsuK+Yrfa7BTzApkWWJvZ8LOSOjRV029cL6B2Vgk0+HU5cbORAAQTBXbDRkPLO4418OdKQRRzM7QRlYkHC9kavtcWu+gqgpplnHtqiDm7Y7Ea8R8RCJym9QtFXcq7usgirHdiMloX8c9J2c2PLu7UFrIMMiSxCCYMjiuonzqe3dGQshLogAddBtnEtCS8mdLgSziDW94A2trYsv8oQ99iBe/+MW8613vOva6D3zgA3RWzhMVw5CjHobPNOIwZHJ9mziOubI9obfepd4V227HC7mw3iWxbZzRlCgMuXZlm1TTiIHNS+e5557zKKeQdz704Yeotfuir5jltOoVsjzjxs6Eqe1xfq2Drove5qWN3nOuxrgs0jRjMnPYHU1BkmnWK6x2Goe+O8synri2R9XSeeLJJ7n7rrsErV3XqFgG23tTklQw+e67vM5ar8Vk7jIstvZVy2C1U+d9f/ooH3viBuv9Fp/7upedOvibOx5XtobCu9TU2ei3sN2AME6wDP0QrX8yc3ji2i5j20VBtEIurHXZG8+5cnPIkzd2aVQrrHQarHTrXLt6jTd++qtv636J4oT//d4/52d+5f3EacYX/tWX87f/+quPVeVhGPORx69huyGWodFp1rl87rAU6njqcHVnhCor6JqQPb1dG7FHHnmE+++//8S/j2cOe2ObqqlxZWvIb7z/o/zbn/41ovhw/1dTFV774L285z/940P/vjWYCDSGrqMqEl/3HT/F7/75o8e06KuWwYf+17/m4kavtBmUZblcwEEsKFcKJ3tT19gdzRmM52xv36TdXUFWJNa7LRo1iwvr3WM7DqGDI4qjZr2CIsslisPxfAFMkKSCr6DxorvOsTOaiYGgqvDYlW2mtseFNQHJdP0Q09C47/IGhqbyyFPbuEFIpyk++8nre1xY69KsCw1809TY6AuddMcTu9+FIuXB3yGMYq5sDYtWmoTjh6x2GsckiBesV1WRT519HXxdkmYMpzZJnFKvmrQKhuvR33w0dcp70vVD2o3qoaHtBz7wAR588MFj3/WcZZrf/M3fLP//17/+9fzET/zEia+9dK5fDiSejbznwZjvDJAVhYplUg9SpntjzGoVVKEfnEcRznCMUa8Rex5tU8Vo1Kn125Ck5GkKpyRxQz8MfVvcrKvdJpOZy9M3B0Tz5BDj6hMZC19NLwxRNQ3ynE7juErasmSnyKIyMnWN8+sd5o6ohNcK1+1Os0arLgwuru2MuP9z385o5hLFMbqm8fXf8VP8zx/8ej7jVS849tlxnLAzmtOuC42TMEoYTOylhrgg2ggvvc/EL3qdpqExtT3iJOWey2tUK0aB75aEUUDr+KzktHD9kNf/ve/h41d2SqOIP/7wE3z/T/4qf/jT/7zUO8kyARFr1izqFQFNNAz1CMQx4i8euYKua0gSNCoWU8mj1ag8pyp/jhtQMTWBWUbm9a98gCeu7/LLv/1BokQ8N1VLDP7f+V1ffez9K+0GcSyQOzd3Z7zvTx8mTo4PAOMk4Uf+x3v5F1/3BdzYHZdIpGbNKu+FJEmJkrSUf1jtNlBVmYYe89IXvxDPD9kZzcnynMF4zlqvdeiZXkg1zB2Pm7tj9sZzMezrNHjkqZs4bsDGSptuu4auaciKXCCGpjheQq9VJ0kykjQTZioFpT/Lcq7vjrE9jyyDvZFNt1kth9eKqqAqcqm0eRCquywOEnSSFPrtxrGh9nTulnKxmiKzuUSr/+DrcoRWUprmxYBWmMFnOcc4GUmSEYRx4XUqWOEnkaGOxvMCMZQKHYzIDxjv7EGWYbUaWA1xwbMsI7AdsjhBr1jolVub+SZxgm6Jbfdap841z2PueFQbQodAiiOkAsYVeAGVVoMsyzAtk8BxSeME9RYDWVVVji06sizjhxGtRhVTF8ONG7tjLm70jq3UeZ6TRBFIEtotvutW4QURc9fDD2LCJCFNMkDi3svrh14nSRK9do298VyoG3oh/U4dWaJEeSzYrweT44Lq/1lf+wNc3x2X9OgFrvcLv/nf88gvfc8xaOTCvm3RzzN0FccLT2XWHe0PJomg50tInFvtUK+Y6LrGudU2T/pT4jgp4Fq33mp+94//Mo88uXUI2eEFETf3JnzT9/0M7/qerwVEy0A46Ih7rYFVYvAX57IznKIoUilVPHc9JOl0N6dnEqqmEAQxuqYgy0Aq8Y6v+Xy+5DNfxf/9vQ+haSqvf8UD/I2/8rKlqAVVVUpi2mNPbwvCmOMfe10Up/z2nz7CP/ji1x+y15s7Pq16FavQ9palfVhrlmUYmoZUMQCJwcShURU4ai8IGUzmx2B4jhewtTelahmEcUIYieFrFCfohnC2mtpeWYVaps7lcyuCZRwnWIbG01tDJKDdqNJt1ZFliTjJWO+1ubk7QVEkdsc2myst1nottGJgeDskqYMEnePXSkgqLNBnQRSzM5hxYeMw2S2M4sLYQoANxnOX8cTmrouiS1GzDCZzl06ziu36DCc2ORKeF3B9d0KzJkAGkPOy+y+d6bg/IUn8fe973y1fI9ofWyiahiTLzLZ2QZIwa1WmN3eIvABZVbAHY5obK1SaJxsQA5j1KsHMxqhVUSQ412vSvrCBbgr6bJRngg2a56iaSuR6VNot0YfPcmRFJvIDIs9HkiWsRh35DNooaZrhhXEpFWAaMo4XCkPlA5ZVSRxz8yOPYg/GIEHvjgus3X35lp9/UgidGQdDF73EIIq5uTfhzvMr5UKzEPFp1ipCkGhvm7Vug3rRY+5EAgt+0s3+ex94jL3xfKluR5Zl/Nef/13++dd83qF/V4uh3OKhX9Cab8dIoFY1mdoeqiqXqKKFd+XNwQy1OkBCYr2wujot/usv/N5SzfE4Sfml9/2F2HIf6GsfV1vcf0+a5TRrVVwvwjRVwjhFUZVTXVeeSSwkWb1QuEFluYClPnDnJq988Z3CDDnPubo95NxqZ6n8w4KYJmz6Tl5lqpZeGIfsK/FJxQIOYjFf77fYKqz6QLAwb/gT4iQpdGMKWzFdw/FC9kbzEv+sKhIT26NeMUu7Nz+IGYznwkVnMCWJU9qNCvWKWbo8BWHM9Z1RiZp50V2bVAtDioqpF2gdgSbbXBWu8ZqmcHGj9wnRPk+SFEmWD5+rL/D/B8WyRALevyaWruEduP8WZh9PXNvjqZt7mJrKSrfO1nBKu2GCpKArCpoil3K6aWFoflI8b7T7yPOFTkRhtpvnJv50jqKqhK63X5UbOvZgfMskXu91IMvwbRdZUeic30C39h9w3TKpr3aZ7wzRLIMkDFENjdDxqPXapEnK9OYusqqQpyn+zKFzfv2WiVwqQMcHharENurwln/41DXc0YTGSpcsTdl7/GmqrQb1/jOjreuqIhYKQxVwpTihUTNL2FKp+IaozM6tdug0DuPGb0VQeuzp7RNxuEGULHWc1zSV9V6TneFMwDsVhY2VzpJPWB4Lka0gFA96p1Xj3GqbWsXkyWu7zN0AxwvRFYXrOyPOrXYxdPVEmNbsgBv9su9a9Fh1TRV47plbqGWK1sHBxadeEQxBRZFw3BBTU7m80X3OnW4WkLQgioX5cDGky7Oca7tjKoYmdFqSlK3BlDvO9UtNlDQTLOdFInvli++gVjFKDf2DYZk6b/n8TxMyzxOHTqFbk2VpqRgqSRK1isnljV5pFbfYNamKckiSNghjxoXK4NwRhK9eq0YOXN8Zc/fFNfrtBk9e32E4dojThBfcuYksS7iez0avLb4D2B5Myr54nufM3IBOq17+zpahYek6jicQRVXTYHO1/QlJ4FBYqBUJWJaFmXHF0BnPXIZTgQaSEO1V8ow0S4miFMcPqFUMbC9ElkSekCRxvlVTR9dUBhOn+HyJi4XU9MKtaqGFvjC8WHpsn5AzPkNIgrpU/neeZcj6Ye+5ffGX5UIzB0NWFJrrq9T6CUkYHX4/kEQR/tQmzzNUQ+f8y16AoqpIsoyq6wyv3EAzDZTiJglsh8gPMGunkz1kWWatJ6jWEgKj3m3WjvWm3fEUvYCqyYqCquv4jvOMk7hh6FxY7zC1PSRZot8WOwelEE86qPgWRDF7o9mpnyf0zkUbY1GVb662RVW/RGdjoSWz0Nw++NvUqxYV0yglhbMsP7WdsoggjHnsyja7wxkr3Tqbq22igr6dpmmJG4/jlNnMZTz3iOMMwxAV5zLtl3surfHwk8uhThVLx9LVMtEv6OYLksZxv0kBbZ17Po2q6BvfrjTvWUNR5EODVxVFmBhn+5BSTVUICiz4ZO4WC5C4B9d7QiZVkiR+/F98BV/0zf+eKEnLR07XFF774D284dUvZDIT8MOnbuwRZxm9Ro3ru2Mqhs56vy3IaZqKpu3rtLt+hKYq9Dt1hlNH6IhPbLwgRJEVXD+g367h+hHnVtvMXI/p3BX37VqPZqPCfO4JRJWi0ahXGMzmTF0PuUAiHeSAzByPJ67tUrWM0jxhc7XN3PFIs5yqZXzCfgtxvdSSybmYJ7UaVW7uTsrnLE5SRlOb9X6Ljz2xxdzx0DWNbrtaeOaKpH19d4Rh6Dx1Yw9ZFnosAgOfEMYpnWaFdr0m9NGjBD+MqFdNTuJsPm9J3KxV8cYzQsdlUfpUu6somoasKmw/8gRpkgI5G/ffdaZhVhrHjK9vkRU6GHrForW5hiRJTG/skAOVVpMkipnvDuldOr9faec5B0WAF4vHWaJRE4OtBX16qfVaq8H42ja6aZa9cbNydvxxloop/sFEeHGjj7I7IhM7OFbaolJx4gXetxDxOcWmDYR2x829CXEQkgYh62sdOv0Of+3VLzyxwlVkiTe+5kU8eWMPTVXY7LcPkZYURWY8cwRTkf3dwEmflyQp13dGRT+2guMKB3DL0IXbii58TpVCg3svCHH8gHrVQNdUhhObqmUc211829d+Pl/5jv9yjCxjGhpv+YLX8sSNPUxdIy9kRy+sdU9E3siyzFq/xRqtE6/lSbHYYaRZJvD9zyCEtrVUGkkEUUwQhHz8yjbXd8es91p0W8ItaGc4p1YxGU4cLqx3+dFv+0p+6bc/wEcfv0GrWeWvvfoFfOnnvIY0Eap96/0WqlLlyRsD4W5l1fF8UQwsPGqTJC1NkwcTh2s7I86vdqhZBk9vDVnvNdmb2MSp0Ko3DUHj11SF9U6Lfke41xuayp889BSSLFOzTIIwxosiqsYKhqERxQmD8RxDV6haJqOpw2Tm0m+J41gYKBu6dqKB9Sci6lXBEF9oFflBdEgsS1MVHC9BL9yqNlZaqIpgtbp+WM6PTE3n5nSMoggTlOHMxtR0PvmFl0iynCCM6V+sFw4/6SEbwWXxvCVxWVHoXNggdIV2uG6ZqLpOlmU4ownuYIykqVitOoHrkcYxinb6VskZTchzMIrqObAdQsdFr1gkSVJW1aquEdohaZKUSbzSaTLf3iM3RAUpKTKadXYm5a18CHt3XMCf2cwHI6Q8p3dhk/pK78TXZ1lGGkVkeY4/mRE6HjlQ73eotJolu/Ty5kqpW7yo/jVVERj8YtIdJBlVS2e6pK22ELgiComHI3IknvzYBO4M6V7Y4Bd/+Bt549f8gNBniRLUgsjxT77yc7jz3ArbI6EZMZu7XNpcKR94P4gYzxxqhahTEMXsjefHXFAWEcaJEKiydKI4pWLp2G6AoeuFcFmGJOVsD20kfYcgjFjrtdA1IZEgydIhKvki/uZffTnXdkZ824/8vMCW50LV78s+51P4/Ne9DJDKvroXhKW7y3Mde6O52DVJsDWYcYfrn0hoOymE6FaH7eGUwEuEllBRNFimztTx0FRFtMwkwWgczYRr1Qvv2uDSeoc0z1nt1JnYHnEstH/azSrNeoW541OvmgRxSprmhdrh/uI3c3zSNKNeMalY4lmdOT6NmlW2XYIwYeZ6AgM+tlntNnD8kFajUmq0B4WPre1BTo6kyOQZGMUCrGsq7UYVXVULm0Gfy5v9kvkZxUm5sH8iIknSUgb76KxIPtAX1zUViX13Ji8IqVYMURAUqofid5OIvZQoitE0ldVeg49f26HTrBJGIkm3m1V0XaeiSoRhXM4CdG3fRvCkeF6laGVFKXvfi/Bnc/zxjJV7xNAvdD0i1yOJbp3E0yhGOeCtJysKWSoGEpIskyYJiqqKoZUkHep3V5oNJFkmmNvoqkql3Tz0WYvIsgxvMiPyfFRDp9Ztn2kAqhkGFz/5JcSeD7KEUTkZKpcmCZMbOyRhiDeZkaUp3UvnARhduclE20XTNTTLpLm+stRkoZYn3HhiizyHVr9Nb/0Oxrs3j39XmpFmOfFkjm6ZyKpCpqn4tkvsB7z8BZd5749/C7/2hx/hocdvcG61y6tfcicP3LnB3tjG0BRU2cIwdCZzl0bNwjQ0IQsg7TMPdVU5tQKVJQnyjG6zxtbehLkbk6QptYrQ9d4dztBUjW6rSr9T5+bumEbNLIeoeZ6fqDz4TV/6Rr7y81/Lr/7+hxmMbV714jtp1C0cNzxEypIQDMFFtfhcwV/DKGbqeKWUgWEIvPVigbudsEydy5t9PD/i4SdvMJn7rHRraIpCFKd4gdjBRFHM0zcHXNses9qt0WlUy8Rq6BoPPnAZrdCneXprWF6/JE5RVBlZlvCDfQ2XvIAQ7o5mVCyjxJQnhTTzYi7U69QwDZWKoXNps1eYDyvHsOeWZdBu1gjCSAhG7UzKdl4YJZiGxoWNHnmeU6uahIeGg5xIynu2IVyfROtRlmFzZfngGApzmUZVwDMlYRa+sHZTZFG46KrC9mBGlMQo2zIVQ2Ot1+LyRpc0F4zb7cGU3eGMPM1Ri8Wg0yqkjSWJ9V4T2w14YnT8+YW/RHrii4iDEK1aJYkTVE1FVhSSIEI+siJmaYo9GBXJ1KCx0sWoV7F3h8hqjSxNSZNUJCZZpr2xyuTmDkkgVrTG+sqxJG3Va1j107dn9u4A33bRDINgZpOEEe1z62d6GBVFQTnw+VmW4QzHhI6LoqrUV3tohoEzHJMlCWa9RjB3CT1PnKeu4QxG1NZ6GLUWkecz296jc37j0Pf4cxs5DLnnnotkWY43nnDzoUeY7WwzqrdobayWC+JCjMuNhLxnnGQohZrgQtpgrd/iiz/zVbzl8zTCOMF2fbI0J0lTDF3HD0M6RpWcfZdxMZDKSpSKH8Yl1nhZmIZGs15hZvu0GoJKf36tQ6dZQ5IE+eLSRpf5ZEC/XadZqxwaGG30W6dqUDRqFl/yWa8SW/cgFFT/vsTNvSmRLFA6W4MpzZpFGCe3bXpxWmRZfmhHrMjyIcGr2w3XD/mLh68wmTvYbshk7nDHuT5xmjKYOoxnLuOpR7NugCRzbXtMmubUaxb337FxSFhO1zV6rTrDheuPrqGq+zR009CYzBwcPySIYnJEJbw1nLG66iMj8fTNIY7nY7se9VoFJAF1PWmnoWsqmiIznMyZOQFhJNBdni/aE5qqsLnSLluCvVZtX0AKqJrCQNnzQ3JAlWW0ExBQYhiZn2hOcTCSJGV7MMMqnO7F4HjCHedWlr534VFbtYTqZMXUy4X/fEFYmzs+cZIKHZaCrDSYzNlc7XJ9Z0QQCQhlmmcoqoIsSWz22zheQFwslLbrk56CMPpLl8Q1y6TSrOLPXeIwJHJd1u67C808nABm23vEQYhqGiSBYGu2L26SZznuZIYsS7Q2V8v36RWL3uXzZEmCrKpLq+xbRZam+HMHs0jEiqYSOi5JFKGdoNh4WtiDEf7URq9aZHHC5Po2jbUe/sxBKbaOiqHBTPT7ZVkmTRMMyyrPKXDcY8kgCUJUvah88hRnOMFq1NCqFbIkZbq1S/fiOUBsDzdX2njjCeO9CWbNYqVZQzNUtELyc63bZG88w/UjDE3lvssbuH7IzHEZTV02+k00VSXN9v0/DV1jY6UtUCo5VCvGISPhoyFghE0aVYusgD0eTMq6qpKkGbWKQbdVx3Z9Lqz3DrnMxHEi+uaKfGJCP0r62FyVmMxcgjCmVjHKVsqiL/tcmF4slPiCUJhBe3601FDgVpEXwlYfe+omf/bQU7RqFVp1Iaz15I0BD9yxQa/VYOY4DMZz3ECi3aySpkJsqd+q0awf3wF2WzUqpk6Spqz3W9iuT1y8Z3sgsPHXtkZ0WzU2V9pM5y7koKgKYZIWrvQa07lHt1mjXj3ZD3QBXey26twcTKlZOqvdOrqqYhgaa93mMQP1hTtWUAhIGZrC9nDGzPEYTgofz16L82udQzo3YRSztTcRvgRLXLCOxqIdt2ihLOYOR/X6YbmR+3Di0Cz0b9SCtGTXfFRFLsWwLEPY7a0bGpc3+6WOvVoYvYiFVPgEJGnG3mBKGCWcdqv8pUvilWaDZDVCVjWyJKF/x3nam4cJLFmaEnl+2fuWLZPQccmShFq3Ta27XLRGeYbJu4yiMjiYNPMjzu23EwLXLh4qSdeYXt0lsF2SMMK3HVbuuoRmGkITJozJ0hSz3ih79UkUo6rHZXNV08CdzMR744Q4DKmqTbIkRVtcqwNoEUPXeOCFd2OPpiSej6Ip1Hqdsk0kJIIPX1ND13jFi+4qkkWEIstikHPgZq9XhUfgSWJlxy+vdOJQcbXXENhpP8R2g5KMsgjb9UszhjzfR2fcKmqFJdaCkFJeQ0UmOiNj7lahFA/03nhOFCfUq4aAot1meEHE7niGXXiE6qqM7Qesd1uCXaorJHGC40VomoquapCJwXavVT91CGiZejm4XBhKPHl9l0sbPSpmhXrVZDxzaTerbK52GOzVxBDfUMuK2TAEZT3Pc6Zzt0TYLH77OE7YGkwJ4wQ/iGhULHptcUxZlhFEyYlyFaqqUCvuLccTMNMoEtrsumoSRDHbw1k5m8oK6WhZkqhZBmGUcLPQ+D6pYp/OPbYGE6xC9jrPRRtwGYdimUetH0b88UeewPEDKobB/ZfXqVZM8nwfKRdEcYk6WpAH8zynUTUJogRTEwNTQ1NF7z/a38GehC/7S5PE01gkKUXTaK72Be4blvabszwnKuy7dMssyAnPPJmeFouWx+TqTYZ6BdU0COYOiqaSJQlWu3lLpufBiMMQdzQhy3IiP0A1DRRVJQlCvOmc5mofxdDJr97g6p9/GKNep9qqU203aKyvELo+zmCMJBXGBufWjn2H1agTez7+3CWwXfzpHK1iYW/v4a6toxe9toMhyzLN/tnx3FDom9/CFf7gjf5sYlGNzcd7BaFDZe54RHGKpgrZWMvQS1bhznBO1TJvS0p0YW6rKjJeGNN6DtQLD37+Yqgb2qOliSQrHFwWg92jPfMkSYkKL9R6xSRKRRtoMne56+IqUztgbrvYXihMoF0PTZMxNL1MlqdFEMXEBcU+TlJMQ2NWqDj22g0m8x1sVxCPLEOjXjUIwqRkeqZZTpSk7N4clE5KVVPsyGRZZnswK1UPw1CQ0+pVHUMXZLWzGnEkxSDfD0JMXSPLM7JUSDPHBSQ1SbPyXGDfZu2oJvkihhObqeOz2W9zYzDm8eu7XFrvcWG9u/T+XXjUur4wOxba6SOqFYOVdgPXC/noEzd45YvvKmGYeZ5j6Tr99uEdqSRJrPfbDMZzvDDCsnT6rbogqZ0BIPeXIok74+l+YlJk2ufWT2xPZGnK7OYOaZowfeoaqqbSWO3RXFs5MZnmeY47npZtlvpK75b470V44yneZIZqGqi6RuT61Fe7Yjupa+Xn5HlO6HrYe0NCP8C0LGor3UPfk8Yx42tbAjqkKORZhrM3wmzUCGwHq15DLRimer1KpdVi/QEBrwxshySMqHVa5FnKfHeEqmvEYXSs1SRJEs31VardiMFT19h80X14k7lIElu73PHql31CFrxPdKiqglW0Q3aHM8HSU4Tf4dz1uLQh9FmEJK/YHt9OEr+wJjQ7/CSlVaucKfE9V7Hoybt+iKYKGnq3VTuElNFL9JHM3RdXeOrGAF3T2Og3qVcrNGsmO4U2+HBis7HSodeq0axbpUPWrUPcF8IlSscvBo9JmnL3pTW6zaqAxnkTeq06V7dHzF1f3HM1E9cLMA29HDLbXoAfxpi6RhDFhFEsdMZVhSzPuLo1Yn2ljaXrrLRPJ/QtwjQEMkZWZYIoIs1yes1a0cZQyLKM0cTh+s6YqqWz1hVQP7mY9SyLmeNTs0QL8p4La8xc/5iU8aGrJO171IZxQtUyMDRhLgOifbg3sQu8e61sE2pHWkWLUBR5qVWiogg0z2mD3Oc9icdhiL03wqwLZa8kjJjvDMqe7dEIHNFuaK2vUuu0yx51rbdfRR4llnjTGc5ghF6rkmcZkxs79C5tHkt+S7/PFhBFqUCzSLL436MM0vnekNHT1/GmMzSrgtSTSG7s0L18rlyQQs+HPC/bIdVOmziMsFp19KqFN5mTZxmSouBN5lQ6++qOsqqSxgmB4+IOp1gNkWDm23vIirJ0UVqgcmrdNlajxjhwaG+u3RLl81zFgp32XC8YcZwwdbxS+9kwVPbGM8GOs8wCCimfScbzYCw0Oz4REYQCbXMSgiaKE1w/3K8cNZXR1EaRJdJMwElrFZO7LqzwkY/fwPMj7jq/SrdVo1YVWOosz1nvN2k3Kpxf63K+UP6rHPDpPC0sQ8fUVeauTxjHyBKs9RqQUxppL5JglCSlpk6aZWyudGjWLZ6+OUA5wrfI88VgEYZTm2bNEi5UvRaNqsn51TaWeXx3eFIsLAmHY5uZ7XGhMLzotevlAj/3fC6sdbi5N+bxa9vF9TiZXSsXEFVVEUlWkfb72CfFQY/aOE54+uaAMIrL1hR5jqbK5WtvN7QCanl1ewT5X+IkniWC4rv4AVVDFwSg016/cI0xdKxmHXlhphwEzLb2SJMESZFpbayiWxah7aJZlvgBZRmlEN86SxKXNZX0ALwpz7JjffU4DPEncyRZptptI0sy/mRGbaUr0DZFEpeLts8i0jTFm0whX+CbcwLXQ5Zl6v0OSOIGyPOcNIrRTEMgWQrUDoCii+ulGTpxGCErClrxQMiKgqqqJGGEauhIioyia7ecC6RJgjMck4QxRtWi0mnd1hAuTVOcvSH+3EGSZeor3VvKJjybkCWZ1W4TVVZwPLG9Xe+f7K70/zomhbTsAqLiLMH8HuWV5eTsjYX9n64qDKcpK50GnWaN17zs7pK8NLWFldzuWODQz60K6vpGv3XiTCDP81Ih72BlKAx+Wzz69BZ+EAt/Tllhtdc8VJGmacbe2KG7skHFFPIP47lDs27RqlfYG8/FoDTJkCXKgd16r8WVrQFeEIME/XYNkJbOdZZFVuh1P/rUNoOJTateoWLq1KomFzd6mIWEx8z1qBUtwzvPrzK1fTb67VN1dla7zULJUWC0G1XrtpBJmqZy76V1Pvr4TWwvIifjvkvrh3xSbzfmjsdwYtNpVMmynMlxDTPgL0ESFzR3iSxNkRWFyPPRqyf3IvWKhT0Yk2pCfCcOAqqdVbI0ZXJzByRZ9I1HE0ZXb7LxgnuQNZXYC0pK/eK7zhKNlS7j61tErkcwt7Ga9WOqinmWI8kSiqoQBxGKKSyasgNkosWxLxYpSZbxpnM00yjRLpHnYzZq1Ptd8jzH3hvizwT0q77aw6hWiIOQ/ACpJUtTsjhh+PR10T7Lc6xWg8ZKD0mSaJ1bY3pzl9BxyZOM9rm1U889K3YqWZKg6BrOeEoaxzTXV098zyIWi+hsb0gaJXQvbSLJMvPtAaquH9KyeTahHvDLNDSVKEnpNKus99vPGLb3iYokSdmb7EsgZFnGUzP3kDoiiJ5t1RRaIIL5FwjiTtEGMQv0Q7sh2hn1qoXrh7h+RKdRpWoabA+mDCcO915aO9EbNMsyrm+PuLozIgxjOq0aL7rrHHqRpIMowjC0so0jHG4cNg/MPtIsK7SyxX2kqQqhL+j/7UYVCWGwYZoCvrioQmtVkwfu2GQyd4uWSE7F0k/cnRyMKE64sTvG8yM+8MgVLm10RQtD1xgdYbFpihCi0lQFVVEwtFtboFUtg0sbvaWa6meNlW6TV1eFrLKhq1SX3O9RnBBGccnsPuk7FobvIHZIuvH/yGPzmYSq67Q2V5lt75FnGXrForEqmIxpHBMHIUgSekVU0rplUl/p4OyNkTWFxmpPDPLCkDzNSOJIiFp1WgSOizMY01zvE3k+gS3s3/SKVSJbQNzY7mRK4gcY9dqhqlHVdXqXzrMzm9C5eK6scg+dg6GjqCqqZeJO54yu3hBEnLU+xoEFSQhzrQtYYJahVUziAybJomUSly2I5tqK0FY5QLe3mnUC2y3PRdU0oiBENfZ1X/zJDLNeQzMNVF2ne+kceZYxSm69+0ijiCQM92GUNZXAdqmvnq59kiYJ4+vbqLrYuqd5jjMYU+00xU5lbj9nSXxBgJjMRZ+1URMVIIhFzR1PSaJiF9FqPq9JfeFUU7bFZBkp34faLWIx3Jo5nnA/snSm88PiXQfFpqBoGy6kFXSV82sd0iw7FYUytV2euL4n7NVMncHI5jFlmxfec75YZA6LtymyTHZEBE2RRX95Qc5ZMIYXUM92s3biMawXGPXd4RxNk9non+332RkIbaKqpVMxNCYFPltTFZJMWKItYq3X4vrOmDBOCgOX6pmq6luxrs8SlmlgmcvneZ4fcn13LDSW8pxGzSx12w9GHCdc2xkxnjmkWY7tBocW0aPxvCXxgzejWati3HVJiGAVq/vCqSfPCjaeadA+v4E3nuKMpiAJyOAi2ciFopogxaikaYKkKKimITzvLp4jCSMkSUKzTJIoIkkzZFVheOU6w6duICuC1bnxgnvoXtgsj1VWFDTLPJSEsjTFm8xI4lhotJxbY7q1S55mtDbXqHRapElK7AeHKveD/XTN8BnPt8h0wSpNggCrddg84WjVrKgqnfPrYnEDNNNg8PT1sqWU5zne1CYKnkKvWNQ6LdHiUZYPVI7Fkdcs2K0AgeMIWYOKdey40lggJRRNRdV10jhhurPHdGsPbzajNRij6hrVduvWx3CGkGX5mODVYhexkGiY7w7JkvS2RcbSOMYZTUjCGNXQqHbbwnjjGYSqyBiaWtDExf9q2nLYmqLIdJr7kLswTMpBZxAlBety/30LRE2SCs0eL4zpNk8f2M9sn73JDEvXwYUkzRnNXZIkRStEv3KEBZ2iCNjcxpGB28KrdgGBU2QhB3CW9lWSZjheSLMu5kw396acW5OPuSwdjSCOqFmmGGK262wPp8xcH0WWWes0MA/ATQWrtVfYFcrHBNoW1zeKE6Fdv2QnkBboloNyFs82dkazE3XbD8bM8cnznHOrXbb2hAH79nB6YrJ+XpL48KlrpEmCWa9SX+0XQi9S2esGcEcTJFkuE2Bgu6K9MJ1j1AWLL/J8nOGY5ppgXzbXV/Amc7zJDKNWobHaI5g7GNUKkiyXVfECDQM5cRgyvnKT5voKsqKQJgk7jz4pWI0n9I5FstgmjWJkVSWY2dR6Haxmne6lc+X3JGGEO5meaGqhVyyaGyvM90aQ59T6Z+sdy4pyqMJXdY3RlRuCNJSmeNMZa/fdhaKpOMMxsqaSZxnzrV2mzTa1XudEJI+qizmDP50LBE2aUe23ufGRR5hvD8glqHfbbL74vkMIIqnQJcnznEqniTez2Xv8aeq9LrV2C0lVufnQY9z5mpefaoP3bCIJj+wiNBV3MqPW6xyqXpMwKguDo4knyzIm17dJkgR/MsedzDAbNdbvv/PEBSjLMoYTB9cP0HWNlXa9fPCDUOysZo6Paag0ahVWOvVbLqiyLAud7LlLGCc0a9axFomha2yutEvDj3a9ckj5b2nk4PoCPinLMuOZSxhGpeb4wuFmNHVI0pSV9nLTcsvQuOPcSilPe9b5g+sFSNK+DHKa5TxxdY9uu0rV1JdalwFULRMvCKmYBhfXhSxup1GlXrG4XEjxHoyDRiNJkpauTlXLwPVDPvToVT7+5HXc1OT+O9apVcwSPSKMXfaF5VY7jUMSzs80kjTFOLAgHNRtPxhpmiFLcqGV3mE6d6lVTOYnKJE+P84+ioJhGgS2UDBsrh1HBGTJ4b61JEvEQYgky2RpSp5motd9oB1hNeqcf9kDTG5sEzouo2s30SwLfzYnjQQ9PktTnMG4JNmkaYo9mtDaFHhrWVGEHnChs7IskjAiDg4ki6J3fBAhA5ypP2s16sf0Y24n4jAk8nxkVcEdCRjl6l0XUIttoaLrTG7sFJWfRBxEjK9t0b10bun5SZJEY7WPUauSJQmqYWDvDbF3hzRWewVcc8Lw6Rus33dn+T7NMKj12tgFVNRq1uhsrtM+v4FSiFTNByPiIECpng3eebshSRyWNz5y/bM0ZfDUNcbXt0jjGLNe4+InvfBQiymNItIkIQ5CkiiisdLFsx12Hn2S+kqPaqd5rEWzO5qVvpxhGHN9d8zF9R5BFHNjZ4ymKTSqIkn023Xmo7MlvAXiAsQWeyG0dPC7F2Sls0ajbnFxrc3exEFCIooT4iTjyvaQWsVkvdfEMnVajYrosc8cHC88RuQCsdDoz2B4vEhcWZ6zM5giydCsm+yObaI4XepZutppsLU3xfFDJAk+5aV3UbXMW9LpF62JhS5+nuU8fn0X1w8Jw4ibgwmTucPFjR66JiQH4jgpYZIL276KqR+qyNM0I8uyY+zS06JZrTB1PKqmTlwMffUDi73rh6gFQWrqeMixQPRUKgbn1jo8/InWTknTlO/+7u/mox/9KFEU8Q//4T/k0z/905e+dtG71asVAtuleZyvgtGoMd8ZYMgVslQI4lutBoMnrhJtD0T7IQxLoaxFaIZB/46LTLd2UXUDqykegtBxCQoUx8EepVGroukq3mSOWa8QON4ZCDz50p6mWavgjael4UWWpDTXPzGQtSzLSIKwcAqSSv2U2c4egeNTL742SxKiIKTe7whlRtMgcFziIESpHf/5F+5GsqpgNoQcZuj6KMX1kCQJzTSXIohq3TZGrUKeZuQSeKMJWSa8EaNAeIFKksR8b8hsZ8Af/9TP8+H/8xtIwCd90WfzGW/9ezROUXa8VeR5ThSEuOMpVquBJMk01verNHs8Ze/jT6FXq1idGv7cZvvRJzn/kgf2H0RJIs9yYj9ENXWxsxlP0UwLvVIhSxKSKKa52i9/h7kTlOJWiiLjeAFRnDCZu+iaWqrvuX6Iu8Sc4VYxmjql8YChqWyutJ+xOFfVMrmw3uPcag83CNjam3DXhRXqFRPHCxnPhIjZ1mCKpWulPv3OcMq5tWcvQVCvWoznLq4fEsUpbiA8NidzF1mWGIwTYR94ZHFQVYXz6x2hQyNx5sp/0ZpYLHRbwylPXt/j7ourzA0dKYdHn97h0kaf6dzF9gMcJ+TlL7wE7H9PkmYsOmoHvTZNTVu6wC2LfrEDc/ygMEsR73O8oLSYSzNBCFpt15m6QjUyTTOu745O/NznLIn/4i/+IkmS8LM/+7Ps7u7ynve855bvSePkRMxypdmAPMef2kiyLFx2VKVAZgj3HM0ySZc4XogHMj8kJSspClmSotSEHVwSxai6RhbFXHz5SwhtITRV7TRZu/+uYzdJ7AfYowneZEqWpILU4/pUWw2SKKax1hNDxIub+DNbSMXWq2eCMd5uZFkmLOxcD28yJ/Q8tDsvoeoaZr2GhERoOyBJKIZONpoyevIas60dgvUNOFChhq7Hn/z3X+Ajv/ReZE3lBZ/1Ou799FdDnhPMHdrn1jEbNabXt0pFyND1aG0uR6scbLGs3X8XO489ReT5KJpK/66L2HsjZlt7/OjnfxXedE5WKBv+1r/9r/zhT/wc3/rnv0znwDzirBEHAeNr20WbSSJ0PFbvuUylJaq6PM8ZP32D+d4QqxkLpFG7SeT5gilc7EpEO6mGPRwReYEQZDINjKqJURV+r/7UptZtC1MRSQz0DqJNclhaIT4T5IwfRAwndolu8YOIvcmcjSMyCGcN09A4t9YVJJUoYnOlXUIRDV3F80MqRY92cT6WqZdWZM92SKyqChfWujgFCcj2hFmErqnC6jCIuefSGrp+PElLkoSi3N73L1oTi5BzoTBYOlZJEEYRNwYToighioXrfb9T49JmnyRNS3clEBXz7mhO1dJLX9Gz/h6yLLPSbbDC4ZbpcGJjmVrplHR1SzA/NUVhYrus95qYho59wuc+Z0n8D/7gD7j77rv56q/+avI85x3veMeJr10kGFFBri99jSRJVNutQ33IyA+wWg1q/Q6xHyArCnEYL3WNMes1plvCbo08Jy1Ml2VFoX1ujdnWHoHjohdyrnLBoFyYKR8MdzrD3h4wTGSc0YTmao/2xXPYuwMUXaO20i3VDxVNO9ZWea5JL6HjFnDEOqppsPfkVaY3d2is9cmzjLX77yy/y5sJVEgaJ5DmDJ68ysrdl9ErFtOtXb7nlZ+HN5kTuQIJ8chv/B6XXvFS3vJT/4bQ9YiDkNb6CpHrMbqyBWS0z60vJWMlUSQWSk0V8gnrqxj1mhDkMnQUXWNybYv3/pv/gjuekh9APSRRhDua8j+/+V/ztf/7P932NXEnMxRNLRJupUjO4vPTJGG2vYc7mSLlEpopBq/znQH9uy4Jl6kiJEmisbaCaplMrou5x3xvRKXdRK9YxQ5sP5lJksRar8nWYMqim9OqC5OQTqNaapFkeU7sBSSq0IdfBnNdliSTNC2/B0SiHU5sgjAmy3I6zaqA9d3GvVW1hLxvr13n+vao/N4oTmjWKqWC5eLfwyjB1G4fcndSaJpKu1mjlefc3JvgJTGqLEhDrbqFH8Yl5PHZRqNmMbW9kvGoqTL3XFxnOneZ2S6aVefcahfb9oXNn6Jyab3H9Z0xrabQND+480nStFi4F4JWOp4fnfj9Z4ksz1EQn+f6IVPXo9eulaqHfhCXGPhl8YyS+Lvf/W7e+c53Hvq3druNYRj82I/9GH/2Z3/G29/+dt71rnctfX+nUBtUdA1ZUQhdF2c4KVom9UM9x0OQqiRmurVbQuiQBMQuTy9xVObLrNdorKa44xmSBK3N1RJdopkmvTsuHHtockkiiSJkWS53CHkBldMKIadaryU8QFsNqu0WZuNk+dosTZntDESClGUBh7yF1O1Z4mACUHWd7oVNItfDatQwG7VD1XC8tUe128Jq1tizpzTXV6mvCD2In/rKtzHfHgjXoMXr/ZArf/Jh/uSn/zef9EV/HfIcWVFYvecOepfPkwPaklbT3pVrDD9+FVSZWrvJyt2XMWtVcb7FOcdhSJblfOSX33sogS8izzIe+uX3njqPONP1yTK82Zw4iISw0szGnUwF+alqMd8dIasyVqPOyl0XjxUAkiRRbTWpNBukcUx9Z0Do+cR+QBrHgvx0IAHXqxYXVZU4SQ45O1mmzsX1Hrbr489sjCwijxSCucPkxjbt8xuFMmXG7ngmsOGKwnq/XQ7+Fma5i0JlMveY2T7tehVJFWYTsiSdigs/qYComDrdVo1xYcJbLf5bUWRW2nX2JjYSEqoqs9p77slakiSx1m0WIloKhq6W7ZLnKixT59xah8lMnOMdF1bZXGtzfXuMlHo8cN8lLMPgd/7sUTqaSNqrrRpplnJhtUu1chhSrCrCcOWgr+iztYVr1yuFzrvKzPaxNA3T0IV6oiKQSaeFlC8bjz6D+OZv/mY+8zM/kze+8Y0AvOY1r+EP//APj73uAx/4ALkboFVMsjRl/OQNZje20SsmrTvOQ5ZT7bVRDR1nT1QxummiN6u4OyPs4Rj7xi6yKlNf62O2m1Q6LSqd21eFOxhpFOPsDMkyIQ1ZaTcxWw3Ruri2RSZLpLZPGolEVFvpksYJjbU+2gmDJWcwJvZ8NMskSzOSIKCxuXpbglnLIgkj7Jt7KKaOJEtEXkC128JsHh+QOrsjkkgwNsMwRIpT6ut9kjDiv77uSw+xUQ9GbW2FL3rn91DfWLklMcqbzNh96OOYrTogkfghVqdB966LhxfJPMfZHfETr/+yUz/vH/zp/y79SI9GEASYS1pUSRgx39pFVlW84ZjY9alvrBL7PkkYYXWazG7sloWDZhrUVntUe22BIhoIDXfNsqj0DifpPM8JHY8silEMDb16sqHHSTG5erPUxQnDECnJaKz3xU5qbBOECaapEbge7nhOv2Zi1itYrQZumDC2PfJMGAYbmkKrXik1rzVFZqVz+LdPs4zR1MUNYhRZotusnAjjW7A3VeXwLjRO0rLtsNCdv9XvcLvhhzE7o7mo/jNRi633mrctmXC7EScprudTq1aQJYkPPnqdqJBFkJBoVA0ubXSXEpFmTlDYDkpomljwzkJYOimEYXckqu4wJkrSkvewPZySZ9BuVOg3TR588MFj73/O2ikPPvggv/u7v8sb3/hGHn30UdbXl7dJAKoxtGpNrn3gY1SRMHp9Yj9A2h5zx2tejqIq5FnOxn19odldbOvX7+0Sbnp4F+ekUYxes2iu9DDq1Wc1EAMYX7tJWm+hmYbwQ5w76BWTJIww212uXbvGCz7ppYyvbZGT01xbEcqCa8sF4wF2tStCZXHRf3ZcmhtiR5BEMXEQIMkymqEf6537toO9NyLPMqrtJtVu+9D3BHe5OHsj0iyj2mlRbS8nTaR3xQIyF8c8+cSTvOiTP4l6v8vux58SeO4Tknji+Xzyp3/amXRW9p66SjXIqBQeiJEfoKgKF++993jL4P6c97zgbrY/9vjSz2qdW+P82voxo4tFPPLII9x///1L/xbfF+CMpoyvCcioquv4c5u9jz+NadZonjeEhk61wj2f9gqq3TZZmjJ8+jpKqysEzopFt725P21Poog0TpBV5dAuZ9FPl1X1lkSmXdVEK+6Fhz7wQbpGlZZeoXNuA8USjM7Adhg+/jSZH1Or1WmZVarVOt0HzpFmcGN3zGjmMJo6WDWLzZU2UZLQqFZKW7zy+4YzjJpfekJ6QcilzX7Zd11IoJ62GC1YkkmSkZOz0t6Xsz3td7jdCMIY2/VRFJlG1XrOHJVuFQfP4dLlO3ji+l55bTb7raWEpYWZR57nYld6G+iUs8RCM37qiPbmXXfdRbteIQceffijS9/znCXxN7/5zXz7t387b37zm8nznH/5L//lia/VqhayqpKTETo+ceBT7bSwB2O2PvY4rc1VjGqlZFUa1QruWOhjy4rM5Po2EqDMNOHAsgQBEnk+/lwMRSutBlma7aMuatVjySUOoxLPLUmScO0JAhqrfTTT4OrTV4CctQfuwqhWUHUNWVWJPL/EHB9tAWiGXuqWxAXqIwoCZjsDZtt7hLZLbaWDVavROrdWilgt6Ot6Rcjs2kOBQDmok27WqmdSYlQ0jc6lc6RRxDBwy8Wuc2HzEH3/aGy+6N4zC2Xphk5OXroxxX6Audo71GtehCRJfNEP/DP+0xd+LfERpIaia7z+m76SwHaZ7w5IkwS9WhHWeUcelDQRW8yD11wzTRorXULHK3c7umXiTWbotQqVVgO9YqJXrX2IaUFSWkAyjxpt+LbDbGuPQkGa+mqXaquJMxzjDMcC65vl1Nd6VFsn7wZrvTbznSFREODuDDn34lX0qsVsaw9J0YhiBXcwIouFvHGt2yQOIhI/JA5CwlwiSTPWe00kJCZzhxu7Y9b6LdqN47sWNxCQR6CEl24PpoRxShTHQs+7alGvWax2GksT505hU1arGIVBhLBme669LY8adTwfUa2YvODOTUHwOcFUZDyz2RnOIJdo1C3Wl7Atn21IksRqr1ni/c8CYXzOkriu63z3d3/3mV5r1WsoqoosyaRhgFmrEbo+3mSGVtDqJze2UXQNq14jjROqbfEgz3YGVLstUdWu9lB0Iex0MCLPZ3RtC1XXyPOc2fYesiKjmSZ5luFP52U/sjz+ilWyK7M0FUiVnkBJKKqK2WpgtZqkkdh6J1mKO5qSJTGVhjBqaJ8/LKFb7bYYPHWd4dPXSaKYWrvJ9kcfp9KqQ57RWO0ROh6VZgN7b7ifxMOoVEtcHFvoeCeaXdwqZFlGNk2UAw+fbpm89mv/Dr/9H36KNDw8mFFNg8/65//fmdEItX6X5sxmvr2HG6dUmjVW77504ntf8Jmv48v+y/fxP77+HcR+QJYLpu6nfdWXcM/rXsl8b4isKqiahm0PyJO0HBbnec5sd4A/FRAv6wBhDMSiZbUEWUlRVZIoontpE6vZELuabgvyvKRpS7KAFC7OdWGevdA5me8M0KtC8iEOQ6Y3doRN3mhaks6yLMMZjLEKSKaQcZiRBELmoNJuUm23kFWVvY8/TbXfobUpNGzyJKVdNRl6Ea4X4scJrWYNQ9cI45gsE4O0JMkKarvMWq9B1dJRNYWL672l7E9dVYiSFFMXQ0rX8wlC4Qk5mi7kU038IGRnOBUDwLlXMmEtUyeIIyqmwe5oxsz2sf0AU1e548KtdXSeTQRhTJoJ7ZNbaZ6cNfI8J0lSMe86oVWzMGlYFo4b8NDjN0t48tz10RRlKab9uYgFJn1hsOEGJw9PnxeyT56mSIqM1Wqw99RVNMuCLKd350V65zdora8Suh7jKzfpXj6HJEl0LmwgyTLuWDAgjaqFahiFDsnhtr4znqIZOmox0R1du0mj1znA/nSI/eAQ67Gx2hPVsSMISO1za4c0JLIkwd4ZoFkmcRiy8+iT5ElC69wG/txB0TXc0YTWhtiGL6rpJAzx5w69i5vUV3vsPPIEwdxhYSIsyQKlkx04B+ErGgrPPU0jixO0Mwrmn/k3yHO+4Hv+CaPrW3z4F38TRVGQJEGO+Gtv+xp6ly8wubFNa2P1lj1xVddZveeOEhq40Lk5LV7xtz6XB9/02Xz4V36L7Yc+jtVpYNZrzLaHNA7ovSu6hjuZle2kyPXw5RlmQZDy5w6qaRxCMTVWeiiaSuT6WK06VrNO6Hjo1QppnAhafrGgaYZBvdfGGU3KRaddtHIWkg+SJAkrvZlD5HnF77bfP5aLajwvUCg3Pvww88EYzTSotpvEfkBrcw2rXqO1scLWaFBe0yxNqVdNWv0OeyS4VZPM93EnM6Q8w6ivC8tCJWE4sQU6AnHfrHYaJyaklUKVz/GCwh7PQlVkkkyQVCqGThjHtBsVdodT5o5PtTCDEGSlLpVCVGs8c3G8gCCMefjpLeonaJPfjp/l4vUz22NqeyiyIDbNHZ+tvQmSLKHrKhfXus9KCRAOOgrFkEusdOonDoJPir3xnChK6LWFbvnM9RlOnTKJi752SFKYUjzbYecixjOXwWSOecru53lJ4mazxvTGNnrV5M5PfTnT67u0z7fIc0rYnIADyrQ310qBKYDW5lppjpxEcYEkOX4aeZ6ThILhSZqWMqAipMKCar/SFJokGwKpIUnkacr4+haB7YjWjWkgqUJDxRlNqDQbzAuItaaQFQAAzQxJREFUoUAtJKTxPspjtj1AVlWseq0U46q06pj1Kr7tiOra9YT/oR/QOtASisMIZzxjcnMHVVNpbq7RPgGKCQKBsPDgPNgCiYOg6OXu92yzLMPeHRA4HpIs8xX/7QcZPHWNR9/7hyi6yoUHX0hrcx1ZlolcD3s4prnaF8bUwzGR66HoOo2V7qEBraKqS8lDp4WiqvTvuCiOLc+RJBnF1FHU/cR0dO6eRoe5BaouWlUc2KSEroc7nIhr4AfUCjROYLuivXR+/VAbptbrCJJSlpNlaQGVTNAqFpqhYw/HBLM5/z9z7x1mS1rV+38q186xc58+YfIMzAxDhgEEwYAiKKIgoqigV0DFe42YBcwZMYFcUTGAIopkRHIeZoDJMyd27p1D5fT7461dnfucGcbf3PU8PMzp7r13Ve2q9a53rW9QNR2lWgZZInCEKqZq6ISuh1HIoagqnfMrDLd6lKcbxGGEMxgjKQql9HoVGjWIEyFgliToadtQlmVmTywyKuSwun3iKKYyN00hlQHOmToLMzXavRFxEjPbONp+TtdUjs81CVKpZ88PWGv1MRUNEtHvLuQMwijCcnxqlWI2nAudCNvxmG1UOL/appVKyx6fbxDHMRfW25h7LGd2Wq/JksTCdA1dU+n0x7iej2nou/TIAQYjm83OEEMXXpZ3nV1lsz2gOJkj2SAhcfXJSzMiPyw2OwOiKKaYM4njmM3u8AEn2SgSC7TleCiyEP1q94bcf0FFVwVixfGCzAR7fvpwKeAHEt3BmNIeh6e98bAk8crMFKHjZdVUY3Eeqz/IBnxJHOOMbQr16q5qGaAyO8Vgo5XqamvUFmf3u9aXi5z74ldI4gR3MMK3baIgIkFCz5m4I4t4ZQNZkTOBqKyqmlSdsoysClp/48QiBWfE9kogIamKcHePIqIwFpodKQFm4vKTxDHDrRaDzTaqqlOaamCk7j2youAMhpTqDSqzUxTqVUAkXqvVZeqyJSLPF7rnOfNQREvo+/RW1onDiCSB4lSdYr2KPRiKhUSWSOI4a0eMWh3ckYVRLBCFIb2VdWauPMnCI67CGY4YbrSyKlo1jaxvPdhoEdgOas4k8sRnNk4c+5o0u+Mowmp3yVdKqLourleri9Ks444sZFUhDgJKM83txVZXicKQSRoPg4BcdRuZEXgenXPLGMUCqq5nMgtTp5aOlNPVTBPfEbsnsYjHaDmD8uwUzmBE6AZpz10ci6JreJZDf22LfLVIbWk+E2DT82I4LqsKiRcTBWF2/KquU16YobYoEpO2Y/AtKwqV2ekDZSjggVPsFWW7daBrKlXHp93pI0URAaCqMq4fMjtVId6xWAqYnySIOXN1hmOb6UYFWZYY2S6KohCHu5P4emtAFMcU04VheaODrqmpXZrKYGzjBwELM9s6NsOxgyTBWmtAHCWsbAo9o9lmFRBEp/5wvxH4Aw3b8ymY25r+kiTmCw8kTENjaNl0B2PiOMZxfB513QlyhsbIclnZ7HLVibnMHnCzM6RUyH3NPXNJki56/g9POyUBz3EJXB9JkTBLJfKVMvWleaxOD6c/whlbQEIUrFBd2E7UsqJQW5g98sR826E83cQdj/Eti9LMNIVaheFmi3y1jKKqmTPOuN0Vvfcd+iVnPnMLf/fyn6d1+jyyIqPn8zzhJ76PEy9/MVa3j2pojNs9micWxbBUVqgtLWTswND3sboCJSGnvX/PtuhdWGX+hmvEcDFJDiQWRWEkWixpaKbYcRwWw40WIGEUCymmvYNm6gw32xipyFGSJIw7feEMNLIyvXZFVQnwUvaq2O3sbCGEno9RyAnFN8veZ0wd+T7y1wAzi6OIXLWc4rgHeGMb4pja4gwgCQRSPrdrIdcLAnbn9AV/zSzlyaWiYVavT391i97KRopYamRFwaVoyI9bHRRdz4ac7mhMFIRMXXZcWPGlPXB3ZAn1StOgcLJC6PkM1lvUl+bRDAOjWMAbjZBUDW9k0Ti+uGv3IKsC4hiFYebk9GAi9H3svkBqGaUCZql46KKaxDFSr0tydplCGFKvlpitLVIo5ImThAvrHUaWQ39s47jifouThJlGlfMbnawHnNN1Yeo7Esl1OHawbI92b8jMlLj/hTyti+uHNFOlSTWnMHbcTC0RxDxitdWjaBqohoKmKnRHI1w3wDBUvCCiUjq8NTdp31zMgi9v6IKwZGjZDvyBQhhdP+D4XAPbEWJhW92RwOpLkmCbxglBJIaie81fvpaQgU9+6V5iEq6aObil+rAk8cDzsNpdZFVDUiQG623mrrs8rX5MrG6fyuwUiqqK6mh9ax/k7EjRG8fDLBcFHX5mmigMiRKhOdxb2UDP59CLefSciWoYgryTJvHV2+/hj571vfjWto2Gbzl89HV/xtz8HI/97ucQRxGN4wui/6fI+9AuVrdPcarBuN0TfWbPZ+mGazGKBYoXcclRNJUojBivrAunesclX6scyEoV19JHNY3t36eIC5Jt3QdB9oAkiVFTp6LJvIAd8r96PkexWWfc6SNJQie9OJUaxUrbxh2Th+eBsjKiICDwAzRdQ9HE/wppEo/8AC1noOVzWJ0BjROLB56vJElUZqYopjuXSXIUNn9dctUSdr/AYH2T/sqm2Jk0xFDxYhGn0sQ7P4skwSgWKDXrWN0+AEbBJB7F2aImBs+WMM+YnyYMQ5AgdD1mr7l8370bup4w8UhlfqvzM/t2nBcL33HZuu+s0OFPQNVVGqeWaCwtHHjdxp0eveU1ivUyiqYx7vYYnV+h/Mir0WSZ4/NNtjoDxo7HqcUaiizR6o7QphUefc1Jzq+3cT3h9pM3de67v0/fvx9VUWhUCwLHnsDcdFUQjGQJhW2ynoDl7X5ua+UCgR/hyiEEwrlI02SCKMa3XVRF4rJD7PKGY9GKEf1+g9lG5dBkPtMQjNqx40IitOgfCMJmcuwL00KvPUkEuzQMIzA0NE1BVWQ8L0BVZBwvpFq8eBUeRTGW4xLHCTlT33VMcRxzdqXFp267DymGUvnwmdjDksTdwZjmySXc4YgojNANg8Bx6YxWCV2f/voW05cLyJ6eM3HH1qFJ7KDQCzmc/hBZVUTf2fNJbIfA9bC7fdbvvJet+85z/KbryFVKmOVtPOh//uof7VJGnEToerzr53+HJ37/dx56HBNRKm9sIyvClkwzdNG7nwhIHfbaKOLej32OwfoWZqWIqusZskEv5HEGw31SqEmS4NkOnXMraKaJWS6gGgZ6zkTPm2J2YBqEno+i6yiBSnl2it7yOvZgSBQEFOvVXRjniaTuRExqtNVGVlUKjSrjlliUkjim2KgdamZ9UAxbHc5+/jbRdsoXOP646ynWa5Tnp+lcWKNQr6KZJqXphujlX6TKnyTvOI6xu31GW4II1ji+iJISNsJQLFaSLIuZwUWON1ctMdxsC4RSKGYjWroNLzbr5GsVkZRkGe/+c7vuyUmCUg2DqVNLooUiS/uuURzHjDfbzDdEkTKRBGiePHbJblMgdg1iziKSsju2sLt9Ss36gdBTd2yTSNszEz2fJ3R9kigCWUDqVEWgVyZVqqYp2I7PdKNMuZAjiRPGtsNGqw9AGIZEUUQc5zm1OMXp5S3yeR1FUVicruEHEe3+SBhLxAmNanEX+iNv6hyfaxAmMbqqomkKpaFBqZBDkWXqlQKmodMfWkiSRDFvoigynh+w3h5QSL1DLcej3RsdihTRNJWlucah6JRwQnY6BJkiSRKlgsHI8ijkdPwgYrpRFtrojkcSJ1x7aj6TLmhWiwfCPndGFMXcf2GD3tDG0BRypsFM2rJSVQXb8Vjd6KGrCpVyHtc7fDf+sJlCKJpGOe39jdo93MGIytwMoelj9YYMN9vZw6Cq6gPqvRYbtXTQGBJ5HrKmY/d6+CMLLW9SnZtl1OnSPrfM1KklZnYoId7zkU8fip/2Rhbd86s0Tx7b97udolS+42K1e+QbtVQjJMIs5SlOHaxRfv6Wr/LG5/yQQMYkAgM9dWqJl/7t71OenSZ0PVFd7wl7MERVVXKVEoHrMVjfYv6RVwu3pPkZhhtb9NfbSDLUl+bBHqIZBpX5aTpnV5AkGc9ysXr9XQvEpFIbbrTRcyZxKgRVXZyBWNDwD9NIPyhC3+fej31OoGxMnWG7y/2f/CLXf8sz0AyD6sIMqmlkOuOByyVX+aNWB6c/QjWFx2j3wipxHFOem0FVFWrH5kUREEZwkTUnX62AJOEOxqiGRrE5s7sNspNqP91gtNFGUoQ0cqFezeYWiqoeKhuQRBFJvI1vV1SBq9/b7onCEGc4JokijGIh1b8JMnJRnJJOpB27rXS7deDn5sqF7PXCgMTDbNZ2tXJ0XWVouZBu0sIwRtcURpbD0HKplPJ4QUgYJ1iuR6EsoygKjhvQrBVZmK6xNNvA0LUsIeZNHc8XWih7GaOyLLM032St1SOMRGV75fE58unfuV7A2dU2cnpawkO0jh+ESOzUL9Gw3P2F186QpP3mDhNiTW8kKPnlgpkm0v25ZrpeQWLI2PHQVZWrT24n7Z1SC5caZ1e2+OxXTqNpouAomjoXNtocn2+SJDCyHPIFnbiVoBzQdt0ZD0sSLzSq9FY2UEM10+RFE0lBVlUUXaF1elmsvs0ajeMPTNVu0jcvzzSZvvIk7nDE6h33oeoGoeehmwa5coF8vUauVtn18ChHbLPiKMoqs+xncUwSx7hjC992McslzHIpE3yfvmwJs1zEKOQPVDQcd3r84TNehDsc7/r5+t2n+b8v+Ul+7AN/R5S6B+2NCa49X6sQR5EgFmkqYRCIymwwxkzpu/3VTayBQGxYnT6aaaDlzNTLs4OeM9FMM0OvrN5xL4qqUZyqU6hVhPZ7Qqah/kDCs2zc/pDq4mw2zOuvbuC7LmahQGm6wXC9RajIJFFErla5pCo/SRKcwQizJCrPqZPH2DpzASlJ0HIS5WNz6feT7GqTHBYTzZSjSDuTKFTFMYa+L3aMl7ioSYqCJEuZiuaECbqz3eM7Dut33EcYhOQqRcadPmaliD/alv/V8iaKqgqIpS52ewVD33d/TqLYqDN9xQla950jAfL1Cs1TS7sSVqWYx7I9RpYYZhdyOpVSnk5/jJZWr6qipAgMyOcM1tdbaPUyA1WmVs7vgwPmc0aWlA8K4cIzdaC5RLs/QleVTMp3ZLtYjoumqiTJ9uzG9QIhhheEB7rw9IcWnYGFJEGzWsxQI5bjIw+30R8j28PU7X1MzeHYptUbkyT7BcceDI7dcX0ubHTQVYVGtYTteNxy93kWZxoUczlmmxVcP6BeLtKolFLTD59jhxjGPEyDTdFPtftDis065aV5eivrBK6H0+vjjWxmLj+OWS6gaBrqA9i274xJtVNs1Jm98iRnP3cr7sgiX69iVvJoOQNjz8P3uBc/j4/96VsPHCZOX3Fylz64Z9kM1rfEtLo3RNvhgJKrlFE0hdrC4dBAgE+/5e0HVtnEMe1zq5z51Be54qmPy4goOys81TDwRhaqoSMrikjkvs/pT30Rdzhi1OpiVEoosoKiqWytreI/9ibBTk1bKJMKLgojNIQOijOyMklbq9NFM3TgwU9qFE1FUlVCL0AzdQLXR1aV1HzBxSyJ9pE7tgSqJ4pwx9YlMVInJiGT3UFzaZ7y7BR2b0joesLLcLZ5SYtCFIoZBJApXh4V+h7LvksJWZYpzjZJ4gh37Gcw2ky32vfZvPesSNzFPON2n0Kzyubdp5m+4qQQzApDfNuleflxBqtb+LZNY36B2uLcoSxbSZKYPnWc2sIscRSj6tq+3YKiCDchP4XKGrqQ2jUNjU5/jJkkVEp5Wt0RcRgSdLuUAg/NttDkmKljUwd99CVdk4PMJZIUcw4wdlw6/TGKJHNioUm9IoS7wjCmNxhTrxQ4s9qiUsxlvp+FnKiONzoDijmDJBEoGkVRKOQM/DBCT+WEQRgs216wE62K7XiCqWoKH4KLCY5dSsRJgqapKKoidOcHFq7n06jk0TSZ9Xafcl701I/P16lUcpiaTmS1Dny/hyWJd8+vYJZK6KaB77gkcUL92HwqFzqgMr+Nj/XGFqHvX/QhjOOYyBesJtXYVh6LggC7PwRg8cbr6K1u4vZH4mFq1ChP7xa6/+affwW3vP0/GW11dumKyIbOt/zKTwgxKV0nCkP6qxvImspgdYPNe89gdwacevKjaZxYJHAc8rWLM9tOf/qWLHHsjSSO6Zxb5tiN1wrInaLsYoXmq2XGrS7rd90PSUxpdprx+TVAojTVpL++xYXPfZmFR12DLuXwRzbdlXWMfB67PxC+kSmczrNskfjbXcxikXytwnC9RRQKu7dis3FowrL7A979K3/IZ9/6r3iWzfwjruK5r/spHvktzxDXTlWpH5ulc2ENRVGBmNrSPMP1VtZHLs82cYcCxhl6Af2VDSoLMweqPsZxLNyaUnlhdzjOoJS5SolcpYxZLhGHIZIsX5IiYhQEdJfXMqimqqnUlua/JjXFw0I1dJonl7LFZ5cmzsiCRGjhazkTyfOxewPhPzthpaaookKlTLlRP+xjgHTgu9kmCkMxWG/W0YzDW5OyLGPu+X0xb6ZqhzaSBFeemEF3e8yUcpRPpJwCWxCUJmYZD0VUSjnWW32G44jN7gBZkvDDgOWNDktzTSqlPKubPXStkmrERNx5/ypT9bKwMxs7xHGMaejZtdNUBcv2KOQMdFUhiCLMFLDqhyGV0u6izkmHlZM+umloWK7/NSVxTVUoF3OMxjaWF9AbWBybaZJPBdJcz6deKXLZ4jRRHHNCktA1hVtv/X8oiY/bPcxySTjEhyruaEx5uknt2Dyh5+9Sids70T4o4ijKKnkQVVR1YVYkwQurkJAZQSxcd4WAkElS5sy+M4rNOr9463v5wG//BZ9+678Qej5XPu0JXPEdz2TxhmvwLKHLEafaHaOtDr3VDUFKMg027rkfWVWZverULtiiMDAeYHV6yKpGaaaBkcsJs4u0it4bqiYU8yZ4+tDzGW22M2Zk5PskcUz92LxQM3RcnN6AwlQdz3IYbrSxBkO8kY1ummh5UygMVsoMN9tEfkhCQqFeAUmwF72xjdsf0bzsuNBd32hRmpmidghz07Nsfutxz6NzfiVb9FZuu5O/+q5X8O2/9XOc/8KXueUd7yWJY1RD54kvfQFPecX3IkVRdn1826G3vEYSJ1m7RpJl7O5gXxJPkoT+6kamJx/6Aieu53NZW0PAI70MbXIpidjuD0niJEOceJaNk2qxBylpbIKyMPK5S9aVOSwkSTr0uFTTQHFcfMdNF5WEQqOWtWAC1xNzokN2CnEcC2mBJKG3vI6sKCi6jt0bkiTJJSXaJEmye1zRNJq1UtZGUBSZzdMy+cI2/E9W1YN3lF9DlAo5+kOL2+9fwdR1LluaplzIMbI9XD+gkDNISDI2ox9EwsfSEG5KuqawutVDVRWMNNVFcYyakskKOZ1qMc/A2nb/qZbyRFFMpz/G8Xw8LyCMI3KmgCkOLJta2o6ZQCxHtouayhVciqmyrqksTNU4v9JmPPZQdZWrT80yU6+I91IU6uUCXhCQM3RUVcH5f412H/mBYLyVimLCn96MsixTaNYZp4iIOAwvwSqNDCs7SQDuyMIZDEVfOtqGgkmyjDsYXdQ5ptis8/zffQ3PePUPEHoBsiJz/+nTu4ZGk2O2O3100ySJEwrVCkaxQL5ayiBwkxhutWmfPp89mO1zyyzd9AhufvmL+OSb/5nY2Z/EkeDKpz1h+zM1NdttQKoWqG33YyVFAVlitNXBtRz0fF60L1LzYLWrI2sqbn/I9BUnIUmI45itu89QajYYtYVzUXdlgwSJ0nSDqcuPZwSXg+LT//ft9Fc39qkhBrbLO179a8JRKX24Iz/g43/+d5z+9Bf57j/+VfLpMFXRBZ56J/M2Sba30jvDH9vYkUS+KmYZqikWnsoONUmr12e42RF4+zihMj99UR/TKK3as2sty4y7PYgEDFD4ckaomookyyzecM2D1rI5KsxSAavbJ1+r4AwFsWTu6svJlQoM1oQshKLrVOcPVs8cd/tY7S4AkiJaL5P7wyjmcYfjI79PEIuAkKAQSnpxsq1JPiHHaanRyPag1KU0e/TiMEES+Y6LnhPIq6NaVr2hhe0FTDeqKBJ0emNye1p7eUNn7HjkFT2FvoKmbOuOiPaKzMj2IE34lTQJHyY2tbrVw7I9TEMlIWE49rBdn3ZvjKYpaIpMqztEVeRMB9yLAmzPP1TLZm8Mxg43XrMk0DZeyP3Lm0LkrmAyHFmcWdnCNDQqpTzHZur7mMs7439WtPeQGHd6DDc7eGMLWZEzH0yAYr1KfWmBQqNKdXHukiRmQ9/fNRhSVCVLKheTS4+CgNAX7udRKHQ1Jq/RiwU27j7D+VvvoHXPGdzRdp9W0TTKc9OgyFi9AXEkWJFR4KPndm/J4kjYuUVBRL5aoTTVgAT6y2vMP+IqvvkXXpUpFoJIanohx8v+8Q2CFJIq7fmWky1IMOkH70DSxDGzV19GHEcMVjcJPZfGiSWSRMLu9tDyJvWleRJAUZRtFIUEg80WveVVQj/EyOcZtdvkqiUqM/udxHfGZ//234Sn6AGRxEmWwLNDDCOWb7mdP3j6C/ns374TEAPaQrOWQULt/pDuhTUC12P5y3fx19/z4/x48RpeaVzBu370l7n3Y5+nc36V0NtfnURhyLjVxSwJlUe9mGew0RLVaRrDrTaf+/t/43NvexejlvAuNEtFIj9I74GQwPMJHA+jVCRJIA4Eu1TLmyiaytod9xG4D9wv82Kh6jr1pXkK9SrN4wuceMz1lKcEaalx8hhTlx2neWLxwMLGtx3GW+2Myu/bLk7aSgSyhHuxna2dEq/MUgEkaJ+5IPDyhTzjdhd3JPRqqgszhH6A77gUpxrCUvGIGKxvCbRWnGD3BvTXNo98Pgcjm4JpMF0vEsUJri9aDzldzxQap+plTF1lZLuEUczibI0gjDLj4ZlGJfUUrbE4U+fYbD1LsuL9xnhBmCXwMIwY2y7FvIGqKJQKOSol0Ua8bHGKK5dmqZYKdIcWm11h06ZrKrnU/Ng9gpiXfQ8pxlxVBDLFNDSOzdSZn6oShRGWFwKS8DztjxmMnSNx7Q9LJV6aEtrNelGw6vZuK/V87gFB2IxCHncwypAlYRBQzOfQc2amRy7LMmEQZnZwSZIwanWwe0MkCXzXRVG1dEAmLNs6py8QhT7KhOASRkRhlG2lc6Uilz/lsZz7/FfwbRt3MKLQqFJbPMD5GQl2CAMJ0ow4jmf/wqu47hufykf/7O/onlvh+GOu52mvfAmN44IROtxo4QcB+Vp5l/WbWSri9IdCcleSkVWZ+uI87mCEpusUmjUC16d/YZXpa68gJ0UCISNJjDs9zHKROFUI7C6vEfg+umEIXRTTYNTqUp2dJkkSzn7uVm55x3uJw5Drn/NMrv76JwsFvwPaQJcScRDy7l/9Q2avuZyTT7iR0lSDpCGEqFqnzyPLEmt33s9bvvcnCHYQrza+dCfvuP21fMtrfowTT7iRxtI8pR0LzeR4dopTQSpmJUn828/+Fh95w99k91wUhnz9q3+Q5/3Gz1BZmMHq9JEkierCDMMN0bNP4hjPdrL2m6SoBL6HZ7v/Ix6qmmGgTe+fAUmSdCS7M/A85B2DOrNcJPR8nOEoYxEeZoe4631cNyODBY6LmvI4zFIRRddxUgE5WVFonlg8spqOwjBrQ2ULA2LYPSFIHbbTVmSZKI4p5XOoskKrO6JZKzI3VdueDygyi7MTDLg478HIJoxicqaeyRTshTf2hxbrrSHF6ji1hcszm0rLSmxT3ZMkgQRyhrELeSMhEScJcZLsqIQvbfgvyzLFgonliN68H4RompL128t5E0NXSRKF3tAmCMIjq/uHJYlPXS5kSuPggdtw+Y4rKm9FwUh752apSNQMGHfFA1ia3rZBqx+bxxmMiOOYSqkozJGjSGC5Oz2MknA975xdobo4J/wk0x7tuNOjOid6wV17LDRXwt2VpZnPc8VTHit8QxGJde9NLSsK5emGYNeFEUgJiqaJ7WR6Mx5/zPV8/1t+d9/5qoZO/fjCgQ+KLMvUjs0TpCa2E01zo1xEG40JXB+ShMriHLW5GUYbawzWNomDAGcwwukNaJw8xsL1VwuXpeX1bKvsO26GiPmrF7yCuz70SfE5ccyn3/J2Fh55Na/+8Nu4/jlfz9rt9xxqLnFUxEHIl//jQ9z43GdNTkiQo1wfRdf46BvfuiuBTyLxQz7/j//O1BUnOP7oR1CobUMCFU2gLoLU1zNwPbQUvfPxv/h7PvrGvyV0PUK2ccX//Ya/oXlyiaf88It29eAD28EZWoKUE4RIktAUD1wXvVhA/h92n3mgoWjarvszDkJqx+bIpW1LVdcuyVVKM028cU+YZHge3XPLxEFI6ProhRzJGAbL62xIKqqmMX3FyQNJTYP1LTzLhiRJDUOSXcnxYvOuqXqZ5c0ufhCSILE4W2N+upZJSYiWWwp93AEhPcjMYRKO6+P5AefWOpjGNnZ9MHaolQsYusZUrchWd5jKCic0ayX8MMJyPFRFptUd4Ych81NVLNtD11TCVBkyd4m66I1ygY1WnwvrHSoFk2svFwxlMZC1UWSxSAShULk8Kh6WJK7qWqpA+MBo285wRH9NaIMnUUSuUsqcdYrNulCIY/eNMTEuTpKEcbtLrzfIXDkkRcHq9Ogur9NdXqe3ss6xG66jPNvEt4XwVOgH6DkFJIiiENIbSBgKBASe8OTM7TAu2PnzCRZb1jQqC7M4vQFGMS9s3UyD9rkVSBLhg7mjb5upDabY4EK9us+AGUQi30vZLtSrBI4ndiZJQhzFwjx4bBPoefLVCvlqBd+yhfSApjF/3ZV4I0sQi8IIRZUxi3k++Nt/wZ0f+NguFqs3trlw6x3868/8Bk//sZfy0Tf+LXZ3sIskpegaURAcWZwkcczWPWd2/Sz0fALPJ18tc/oTXzz0tVv3nSVfK2Pkd1ulybJMdXGW4WY7FaPKUZ4RLbn3vu5PD2z9+JbDe1/3Jzzlh1+06+elmSkUTbj9HH/0I+itbuA7LkYxR6lZe1CY+f/JMAp5crUKVrtHEkdCZ6YiAAT6nmIpCkU/WzlgQJqvVYSEcm+A3e5TmZ9By5t4Yxt7NBbGGyMLf+wwdrqEXsCxR123CyRgd/v4lo2ZavpYvQF6Loc7GqOk8658vXrkgDiXMjrHY0u0NkpC7XFkOWx2hkRRTLl4OEFnb/SHFpudIUiw0e7j7TQlSbZHXrVUzz0II1RVydyRtjoDTq8IgbS5ZpUgEsiXctp3LxXMSzqOOI5Zb/eplQvMNMo4XkBvZDM/VaVWSjWQbI8gDLn6xCzFwtFdiYcHYriyThxEzF51knG7uz3oOEJXJEmSXaJOAFZvSJLKxmq5HPlqed/rA89jsN4Sfb7RmPqJRRRVZbAh3O6FFGFC4HqUZ5sMt1oEnsv05ceRFJlRq4s9GLHyxa/yifd/kvU77kWSZa79xqfxjJ/4gXRIKlAN1fkZQt8XFm5xAkmMnvawfdvByJmoqoJZEeYBm/eeIV8RhhKDtS0kWc567nZ/KPDa6UMwandRTeOSsNP5aoU4jAS0UpapzDTFDiTczQqUVUEMApEATj350cJ9xnbwbQdv7PDhP/rrQ2UIPvN//4Vn/9KP8yP/+pd84DffyF3/9SmBpKhVeNZP/Qif+r9vF0n6kL6nrCjMXXvFrp9p+RyqqeOObbFoHhaJwOIfROJRdX2fXknguqlY2MHRW9nYh8OXZXnXwtk8eYzA9TLC0t57LY7jBwRrPCqSJBGLYHo+lxKSJIm+datLglCetPsjjEIes5SnPDuNLMuZWiWIuUptcXZXW0iWZarzsxilIrEkka+UsvnDcLOFMxojKXLmcOWOxwzWN7E6fZI4prY4h5+2YSbHpagqRqlAsVnLdkkX04uJ4xi71SEY2wQAto1Rr7HW6pM3dBRTpj908IOI2YvoocRxzFZ3RCEnWmIzjRJfWF7GThFtpqGhazskAfa0XxRFpl4tMrI9ivnt341tL/M7vdQIwoggjLP3KeQMRrbw7l2YqZHP6cK0wxRkq4vFw5LEZUnCqBRZu+Ne8nVRgVrdPoHn7/I23Bs7t08gVvsoCMiVS3jjrtDlnp/ZrojDMINYIUHoh1idPuWZJoValTgMsToDnPGY+tICkgyBG1BoqBQadSq6gPjd81+f4tN/9FaSdEiXxBG3v+cj3Pfxz/ETH/x76kvzuMMRvl3G6vQEdDLtKY47PeIwyqpBzTRonb4AUUSYkksUVSPwPMbdHguPuIpcuSR0TyZ6K5KErKr01zazwVRpunEghnry96WpBsVmHbs/ELoim21hhlH3UQwdSZIIPE843aShmybNE4v01zYEJLIrIJGHRRxFSEDj+Dwv+KNfJvQFrdssFUjihKWbruXjf/kP3Pov7zvw9Yqu8fQff+munxmFPI2lBZzBkOOPvYFzn/nSga9tnFxk4RFXIskygSv6s5Oq2XdcYcO3Q9VPNQw00zh0CGsULk7uOUozPfR9+ulCAFCenbooIuawiOOYwdqmaEUgkSsXjvRy3fm64UaLXKUk2o6tLkmUUJqq445t1N6AXLnIYH3brSgKQgZrWzRPLe17P900UFVFIMhUhTgUg3khuhUTOA5+qkR63ye+QL5aQTUN+mtbNE4dE+xOTc0WJFXXhO3iJYp9OYMhnrXdR3eGY1wkoe+vyLh+QLs3xG9FuH5Ao1KkWTv4mieiE5bdD1P1CuWCIADFUUwSB6xt9ZibqmbyD3tDsDodRpaLpsmU8hPC3CWdzq73SXa0luI4RpbIzDQOO4fD4iFr6o1GI172spfxPd/zPbz0pS+l1Tq86qkuzGKWCgR+SBInKJoq5DstO3sIdsbE9MAs5nHHltCVGI0JXI9Co4Zq6JilgnAk3/H6KAhFLzB1+VF0MUxJkoTQD2icWKQyP40sSciqoPxOX35MOMOoCqquU1uY5X2vf2OWwHeGN7b48B+8CSCj2R+ohLejzRB4Hu5wjFktoek6INE6cyHr8Q/WtkQbwDSIg+0+s9Xt4w5Eny70A9pnl/Fs+8jvJHBcoX+SzwkGZkJmYuHbDqXp5oEtgSiMGLcFU/MwbWsgMzmoHZunvrTA9OUnmL78BHEYYRTyTF9ximf91A/zfW/9PcqzU5mDvWaKhPqiP3stC4+4atd7yrJM88QijRPHeN5r/w/qATRy1TR40Z/+Ooqm0T67TPfCGq0zy3QurNK9sIbdHzLcaNNf3chQKZIk8cSXfue2euPO9zMMnvSD37UrSXqWTXd5je7K+qGJf2cM1rdIAKNYQMvnGKy3hLnEITo8SQrvPCjs3gDfdjBLRcxSAXswEiYSF4kkisT9pwh0lmqa2T2p6roQFktNIrbt7IQI10HHomgaZqXE5l2nWb/zfnor69SOzVJZnKV3bo3WuVXs3oDNu8/gDix8xxGa83lTMIlNA29s4Y1tCo3aA1ZqDFxPENImx6OqJH6Qbexa3REJErVKgWLOoDMYHyoUpSgyhZzB2PaIohjX82lWC5i6ytnVFhfWu9x+/yq33X2B6JBhve14OI5PbzSm1R1yenmLejl/ZAslSRJcT3iaTq6xrqk0KkXGtsvY9rBcn5nGfh/ZS42HrBJ/5zvfyZVXXsnP/MzP8Pa3v52//uu/5ud+7ucu+roJxChJEg46hZ0VThzH4qFOwMibVGaPhr9JqXJOkggSiT+ysPtDvLEwRcjXhDlzPjW+VVKT3/rxRTTTxOr12bjrfjrnVw45eLjjfR8l/J3XAEInWlZkxu0u+XoVCUksUIW0F5iqzeUrRdGTtl3Gm52MuZevlonCCN92yNcq+I6bPrxiEOR7IYM778Xpj1AMHZKY+WuvzPqKzmDI6lfvIVctM3/dlSlaQdl+YA0dVdNoHF84VI899H1C32e43qK6MM3jXvztfPiP/3rfIqblTJ72ypdkbYMJm9PfwT5VNJXqwiz5epXfXP4MX3n3h7nwpTsoTdV5zAufcyh8VFYUivUqV3/9k/k/H/tn/vGVv8TybXciyzLlxVle8uev55pnPYXWmQtCB10TGjzrd97H9OUnMv0QdzQW5LH02L79t36W+z/xebEAphhoo5hn6vITPPf1P519vm87dJfXxfskCd0LazROLOxDosRxnF3DwPWyBVHYtQlnqDiKkWSZyuzUtoeq47J1+jzEQnq5Mje9q/0SuC7KjhaKqmkHwin3XTdVFdo5np/ey2ImIKsKge1gFqsCi79DwmHS2jhQ5tgX5LLq4iyaqRNHMeN2H0WRmbn2MqZm5rD7A5I2GAUhYeFZDlEYUqiWqS3OEQWBaKcc0PtOkiSzSlR0TbTHdhyHauiMtjoYxYJYbIKAcrOGFCT0xzaDsU2pkKNRKWY49p2L0USvXdE0JElitlGho4yxXY9izmSmXua+5S1qlUKmgbLZHTIYOdSr+4ub7tBiab6B54eEYUQQRphHCF/FcczaVg/L8UECU9dYnBEQx2athB+EbHWHaKpCGMWHPpOwrbR4UDxkSfzKK6/kzBkxpBqPx6hH9AQ37zlDFEZ44zGFWhnfFl98sVHb10vsp04rRrGQmhM41E8dQ9E0hkYbu9tH0TSiMCSXynImifA71AyDQr2K3e2TSBJmpcz0lSfTG3tys2ss3nANoR8QBQGe7VCaauBZNqPNtpi6X8IKWZmfYbjRJnBdAtejc+YCtSVRoaqGLmjifkChUcXuDYj8gPJ0E2/skKsUqKcU78D1kTU1PR6BWc5VSriWhdXpE/khhUYVq9MjcDxGrQ6l6Sbv+N+v41Nv+kdUQycKIyqzU7zkr387a+OAGLiq5naLZm9EQUD3whokoJcLbJ1d4eSTH8U1tz+ZOz/0CVJMJKppcO03PIVv+/X/ve89VENHS00jZEUhCgJKqTb8o779m3jUt3/TRa/lJJIkYerUEj/0tj8WZhY5k17ic+111xFHEUkUoaQJelsSNtn7Jtl/mqUiP//Fd3PrO9/PF//p3SBJPPZF38ajvv0bdyUZezBMkRziZ0kc446sLIlPhs7OyEKSZcozTfScid0boOgakiwz7gwo1MqZOFl/dZNm6hc73miz0BSJ27cdhpvtXW1EPZ9jtNXJjL5DX7TAQt8/NCGC+E6ri7P0VzcBMEpF9JyZ8gvy2cyptjBLb21T+Limipd7I45juudX6a1tYOTzqLpOZX5a6OxLkKtVaaTFThKD3etj9waEQYCqGzSOLwhJhSP6+Vanl5qy6ET9Eb693Q5NkoTQ9XBHFoP1FooqM33VZeTKJfKSRLWcR1VkgjBOk2AECZnFnNUfMN7qCAMIXRcOYJrGdGMby95rr5NEwoB6EqEf0e6PURQ5k77Nrm/6/xNEy9ByjywiR5aL5foZusR2fXpDi2atxNh2GVkOzapYgFrdEaoiH2jp5rh+6np0cEjJxdgwB8Q73vEO3vrWt+762S//8i/zmte8Bl3XGQwGvO1tb+PEiRP7XnvLLbew/LHPoxoGhdkpUXU1qyi6jl7cjTRIkoTe+dVdmPHAdijNTqGaRkqAsYXvoq6iF/IEtisYa3GMkjMoNhvEYSikSBGsR7s7YLi+RTAWyI/6ZcdFNRdGxGFIdWkeuzvAH1sohs47XvxTjNY29188WeKqb3k63/jbP407GGXEiigIiMOIwlRjV895EnEU4faFEcKkShIVhISa08UOYX1LJARFIXRc7M6AwHFwesMUK5zQuPw4umnypb95J3e+60OEe+Q41ZzB89/6O4KKLkuESUzj2AKyKoyYb/u7f+er73gf/thm7sZruPH7nktpdhotbxKHIf0L61itLqXZKYZrm6x/+W6iIOTU1z2OEzc/5sDWxOT8/LFNHEbCL/WA6XoSx3ROCxJJ/bIl4UeZIlz0UgGjVMC3HKytDlpqc+X0hwRRSKlaRSvm8YbjjM4fhyF2p49iCi31OAyRZZnS/PQD0ukGwcL1LTtr5QS2i1kpkkuhjHanjzccoxVyxFFM6LjImspwZSPVIRfXvpJasCVJgj8cU5gRBs6dC2sUd8AiA9uhmiY9SCUa2j38sQ0kaIUckR8Sh4L0lauWs2M5LCbEnuy+V5V9z1YSx4eSf9yRhbXRwhmMUU2dyBfm0ma5KPTUPY98qYg7GDLe7FCan8bu9Akdj6mrT1K8CEkviWP6F9ZQd9jTBZZNaWFGtH5sMYg2inniUCh06vkcxZltraMojun0LWxXIN2makVyhkbo+4xWtlDzhmCTej6ypmTS19k5ui7rHYuVrT7FvMHQchlYLteemEVVZHRNYbZRzlB0luOx1R2jqDJxlOz7/d7oDm0sx89UGP0gQldlpuslOgNx3Dt/Z2gKU7X9MhPLm300VaGSV3n0ox+973MeVBI/KF71qldx880388IXvpC7776bn/7pn+bd7373vr+75ZZbmJJSyzFJwiiYYuuY4pwrc9O7Vu/O+RXiKEYzDeLUVLh58tiB1Ujo+8IgIWciKwq+7aDlTMozTYE+8DxGnT7jVptio46sq3TPr6Hnc0xdtoQkyVQXZlBUhdaZZQYbYgB09jNf4l9++vUQ7ugbShJmqcAvfOk9TF12nFGrQ+fcihiqKQqB7VKcqXPs+msveu0mMrKQ+lo6Lr3VjWz7nSQJnQurGDmT1TvvJ7Qd4iShsTRP9dgcv/no5+xL4CB6mk/90Rfz/N99DUmScM+99zJbrtFdXuVtL38N7bPLRJNtuiShGjrf/YZf5RHf/PS0r9zCHY1onjiGZznoOQEzK89NYRTzVOe2PUUnWPWdptaHxa3vfD//+MpfwhtZIuEZBt/4cz/KTc//ZiRFiCmVZ6dIwgg71S+JwpD22Qssr6xx45MfT+C6KZTSFQ+pKh7S0PeFmbOmUahXHxRKZBthlPbTFZnG0kJ2z7XPLqets1R6oTcgcD1KM02hFy5JYid2bB5FUwVTsdOjtjhHabrB7bfcxrU3Xi8Ygn4ASbJLoz4KQ5z+UMgmFwupRr2HnsJVvZFF/fiCKDwOgQl+rTFuC1RWFASMW720wlY58ZjrkWSZWz7+aU4sLSEpctpvF/dfsVnbZ14yiew+l4RHafv0+V06SRP0mGYY2IMho812xlCOo4g4jA7V8t/ZhvEsO0XAhWKXHwiNoMVHXp2ZUgPcddddXHnllVxY77DRHjAY2xyfb2a0/JHtsTBd3eVr6rh+apYsUS4ejUoZ2y6rm10KOSMdirrMNipUywV6gzGt3jhDqFiOR61c2DfUjOOY+y9sUcwbrC+fOTCJP2TtlHK5TKmUGh83GliWdejfJinxwB4McfoD8o0qqiYkSuMwonlqKftiK3PT9Fc2hGGCJFGemz50Ozmhp++0G/PGFuNWhzgMMUpFBpttnMGY0lQTVdUozzRRNZXSVCObgnfOrWQ459XbzjJ1xQlu/umXcf+/f4SNO+8H4Pjjrud5v/mz2VRfMw1G7R6FRgVZkonCgDgUA9mLiSXtNVmQZHnXMDQKQspTDXzPx+72CByfXKWE3etj9UdCT/qAJB4FAXd/+FPZojhc2SRJWtz94U+zdf95kp1D5HT7+m8/+1ssXHelgF/KCkapSOvMBaEKmGLzdyaMJEkYrG/iDMdIspwSkOYOVZ2880Of4C0veXVmwAwCd/6uX/hdio0a137jU4Uy4WBMvlbOBtWh5xM4PnpRwNokWcLuD5i+7ASk/VBJkjDyuUvSAz8qVF2ncXwBLx1oGoX8HglgjcDxkHPiOkRRiKyks4c0QRSaNeIoZLC2iW871I7NCz2UwQi9mMcbC0VAKcW1O8MRgeMiqQp2bwjpfeyOWimKaJsDIclSSo0Xz5gky1QXZh+wLO5RoedzjNs9MajNmTj9AbVj81lLqTQ/zdRlx7OBvtXpCUXS6GA/04lK5OQZNUtFctWyaIfqOlEQppBFca9qhp4iR6KUYdzNwA+B64mFWhcckL0Ltawq2J0egR9CHNNZWUPXNAq1CrlyierC7PbuI0moJCG5vMLqONrjG7K/vs2Z+iUbQBTzJtP1Mu2+GEo3q6UMMlgu5hlZHqP0OTB1jdoByoiyLGPq2v8/zj4/8RM/wS/+4i/yD//wD4RhyGtf+9pD/9a3HQLfJ3QDwtBHsQwSPSIMAnzHEf3hNPGpuk7j5LEMf3tUxTFh0O00+gUYt/tZL1jV9bTP6KGlLRktZ5IrF1E0sbAAGPkcZqnA9FWnUGSZK591Mzd/+7fQPnuB4swUmq4JM+TVDRqLc6Lin6qLPjYh5ZQK/mA2OpppkKtVcHqDLEHVjs3TXV5j+opTFGtl4UAzthmsbR7qRASQq273AIcbWzROneLWf//A7gS+IwLPZ+2Oe1l6zA3kSgUUQ6d99gKRJxKoUcgTBWGmq+7bDu5wnMHpQs9ntNXZh9OexL/93G/vSuCTiP2A97zuT7j2G59KEsXImiqG0baDMxgLyKWhYegF4ihisN7Ct2xIoDjVoHCED+mDCUXTyFfEPRgFAVavT5IIKGJpqkF3eS1DOhWnGoSOKwZ0hi58UStlqvMzREFEZX4609ORJAm9kKN5cjEbuo07vSyZ2f0h7sgSPAVJQlaVDGoXB2E6CPSEyNpUI507hPTXNpg6dfxBIxz2hp7PUZ6bYtQSvdjasYVdzFhJkrJnsb+2iTe2UQ1dQIVdb3eiREBtkx2mIs5wRHV+hsr8TPrd6rsIc5ppUpmfZrjZZtzuCleoXI7l2+5AVjUqs1N4Y5vA9akvze/63jXDQDVNotCiu96iWK1gFIuohoE7tghcL1vwhpttQs/HLBZoBDHrZ5eZufw4yMI309Q1bMfD8wM0Tc2q6kuJ4dimN7JJkoRSQSgkTl4r5AJqeL54DjVVTgfl7LuH56YqrLcGh37OQ5bEZ2ZmeNOb3nRJf5urlUm6fUCie+8ysqxgzE2hKQZOXxB4hMGBUBBUdR05tbE6asuumaYwKN7qgASe5aCoCq5l462Nqc7PUJyqY65vYfdHhI6PXipQ3SGmv9OpWlZUZCVBzxlIVsC400PP58mnN6JZLjPaaNFYnENRVcpz06KXahhEvjAwfjCSpRMz4Fy5lO1aFE1DVmRkOXWHSa/R4o3XYJaLWVW2M/RCnqf+yPdk/05SP8fwAPLOJBRFQc2Zwlw4fUjL002KzTpffc9HOPf526jMTvOE7/sO6ksLWV91ErKq7BK9uvODH+ddr/ldVm67E9XQj4Trdc4sM2510XIGlTmxCFZmhbb8BAPdvfNu+qsbOIMxzVPH0PM52mcv0FvR0E0DvZCnks5aHoqIgoDOhVWSWBQGVrtL/fgCjeOLhH4gDIE1jcBxGLW6AqVSzFOabiIrSjq4F4uW6EOLClvVhbrk8q13sHL7PRSbdWavugyzVBBiab5grnojC0lWcIYjrPYA3dTRC7nMjBsECkhI70YPqf55vlK+qKhVFIa7NFFUXTtQEyX0gt1EqlR+OV+tpDMbeV/yypWKyIpM4DgU6mInMtrskMQ+kiKja7l9CKTstZUShXqFJBYqpr7jIrFtfg2pR23KKgWo1kpEgY8Ux+TyOerVIkPLodUdoigyYRRTKxUO9fLcGVZqJqGpCq3emNMrLWYbZU4uTO3SIjcNDc8POb/eIYpjZElifqq2i2ykaSpL8w1a6+cO/KyHRwBrukl/dRNJFbKe63fdz2B9i9JUg+rCLJHv013bSsWMJPL1CqHrZQlAVhUqc9MkqUyonvbAQaggmsU8URjRPb+CUUoNDjbb9Fc3qR6b4/KnPAZFUUkiIZS/Ezqm53MoukZ/Y4vAcRi3O+SuOIU/tqnXGjiDUfa3URCgVrd7WJW56Uxq06yUKDZqX1NltPfGrMxOM9psp8cgoekatWPz/ODb/og/ffYPZE42IIauzVPHeOyLvi17fXlxlu4d55i58hTD9a0DPzPwfK5++pNwhmPk1D8ysF1+69nPZdTqCPyvrvG+1/8pz3nt/+YZP/4DJAmZzZhvOxTqVdyxxZf+5b38wyt+MVs0Loa3lhSZ0lwTs5Df5eY0SQb1pQVyW5tohkmhXsEo5Ak9j3GrT21xRjysls2o3T1UMzsKAuFdGceYpcJFBazcsSV0xlOMc+C4WN0+1bkZ9Jwi+ufnVgScLUkozTR3tXPEvb6RLbLF6Qa9TaGaufqVu7F7fYxCnsBxWfnKnYK+rir0VzfxHEdQWyTor29Rm58l36xRqJbZuHss7ABLhQxl9VD3xScaQ8CuZ2x/bBNX4GAPALNcYLjZxlSLJHFMHMUoqpKpUUqyRGVuehcj2eoP6K+s01/dxLc9yjMNZDkVXZvc5xwMHitNNxisbaVInzaFRl1AlDUtG8hLkoSa6uJMSEmFnEljXjhBxXFMuzemmN8evvbH9i5I4mFh2R6aqtDpC8OSejkvIIetPmEUMRg5RKlzkR+EFHIGOUMnjCJWt3qcWpze1W8/jFMAD1MS75xbRTUMcuUiw2IBqzNA1jUhVCRLDNZbyKnAv0CorKEYKsX0i3AGI5Zvu5NSs56pDzZPLmZbVlXXkeQQSd7GSFfnZwQb8OQi+hEPrqwoGHkTq5sIh5hKCc00KEw3WHzkdVz44lcZbG4hSTKqrjF1anvQspemfakxWN/iy//xIQLX4+pnPImFR1594N8ZhTzzj7yacatDEseUZqbIlYpc9XVP5Npveipfffd/CYEthAxs+/QFPvDbf8Gzf/HHANF20BYdHvvib+PM575EtEdoXjUMbn75C6nOz5CvVQReWdP4k2/8PmFAnL73xLruP37pD5i77kquuPmxjFtd/CAQw0bXY7jZ5u0/+etHVv174+pnPQV3MMbpjVANndrCHqNiWcYsFag0pjJPUnEscYYNV3OmaLMcELuqalnG6g6oL80f2UueVOCTkGR5F2xxJ8nHs2w27z5N87LjFBtCqElRVepLCwSeh9MfMtrqsHnXGZq5Iv2NFvWFWZBk3NEYd2BhdwdMXX6C4UaL0nQzdV2ykGUFVVdxun1006A41SCOQ6EvrmlUF2YeslYKpGznC2uEYQhHOB0pqkqhXmXc6QkBriAgV6vs24HmqxWSKMbqDdIe/swOE/G0Rba2iZaCFoQjUQezUqZgu3gji2Eco+aEfnzo+URhSL5SOtC+MVcqoh5X8d2GKPiSBFXXxPey0/B6doq1O+4lcB1Uw6R5aimb50xYnnuv62Et0olJhO36uK5HEEZ4YUDBNLBdH1VRWWv1WdnoCNx6s0ohr7O80eXyY9OMbZeBZWPZPnlTZ35aFIGO67PWOpw5/fB4bJIgyULzxMjnKDRrlKcaNC9byrY4pTQZSpIkROljMTyxe0MGWy1UXSdXKeH0R/gpA3HmqlMZFX0iKTvpt4V+ICyvLsHmzR6MdpkMeKMxiq5imCYnHncDdqefuaf011sockcwEh+AfO4k3vPaP+F9v/FGoZiWwsKuevoT+JF//YsDq8RcqbiPbn/uC1/mrg98Ikuyk/Bth/e+/k950g9+F6quMdrqoksypekmL/6L3+Q9r38DnTMXUDUxKHzyD72Qb/3VVwPb/pGt0+dZvvWOfe8NQj/lQ7/3V8xdc0WmtOjbDlanJ5x6LtHpRVIVzFKBb/ipH840SQLHpb+2SeP44r6/LzZqhJ6fEUVyZTF8A4g8H3ds8eE/fDPtM8ssPPIqHvuib8MsFVP/zm2TkNDzsbo99CN8UI2i0NCeaKYEnk95pkkcx1idHu0zy+TrFXzHwWr3CP2A/uomgeMKpFVKNLG7fYabbUE26w9pn7nAuNOjWK8IK0JVFtXmbJPK3AyRH2AU8gw3WkJWuF4Vw2tJZtzq0Dx5jNLMFCTJRTXC4zjOWKATM4aDXrNT+8UZjIiiKKuMPcvGST1xJ5EkCe7YwrXs7HWl2al9LZgoFPDIXHVbSnmicjh5f1lRSADf9ZBTjXIQ7b3y7BSWruH0hixccQI1ZxL5YvdhFPKHnrtmCvPvowbdTn8g3LNKRTHP8vxsV6EoMqUUemimglg5XeiHTyCaOxeEdm9EZzBG11RcP2RkOURhRKcvqnnH85BI0FSVSilHf2SRMzX6I4s7Tq8wsjzKxRyVYo7ByME0dGrlPKtbXbQj2mQPSxKvzk0z2GjhOR6uZVNsVFm49nJU08QdjdGKhUyBLgpDdFPHtV1sy8FIe2+R79NdXqecDncUXWOwvoVREDRYSZKozs8wanXSm9egdBFHkyyOGEZquk5lbppxp8eo1cEsFTMZ18aJhUsy5J3Ebe/6AB/4rT/fhyy55yOf4R0/+Vq+589ff0nv84V/+PcM4nVQ/Mcv/z7Lt97N6p33YJbyXPvMp3Dzy17IK9/1V5naX31pIdWU2X3Dt88uZ1T9g6J7fo3Q8/Esm1y5JBY3KTWrOOJam6UCuVoFCXjUdz6br3vF9xFH4bYXYk7cCwex2GRF2cUGtPtDxp0+sixx939/hne8+tdFwnVc9EKef/2Z3+QnPvC3TF95ct8xXWzurBmCuGKlPpflmSZ6PpcicizUnCEGk70BpZkmqq6jGTrrd5/G7g3IV8uUZ5qpJESMkc8JHH4QUpqu01vdIl8pkkQxJx9/PbWFuUyVc7TVJolj7P6IUrNGvl7F7vWpzM1ckpbKJIYbW7gjG800GKxvsX7X/ZSmBRqrPDOV2tz59FbWs8VakqVdPWpZUXZJYiRJQn9tk+75VaFXrmmUmjXcwYhcuZQdmzMaM1zfEt9jSjTS80K3RdW0zHIujmNC16V7YQ1FVQg9QfTR8iaKKobcxWZ9exF5gBT+gyIKQtyxTWHH8H8iNz3ZScw0Kmiqiu14lAs5GlVRDAw2WiRRhJ7PUZmbRpJlekObUtp6MXUNSYKTi9N0+xYJCSsbXZbm6/QGQu9ckiQGIyf11hSaUpaTwhoLBiPLoVQwieKE/AFCb5N4WJJ4bXEO1dCxByNylRISCVEYEQxH5GsVio1aag9lISkKU5edYLCxRc9ZJ/QDaotzjDtdQtsmDqvEoagYokDQbJF3U3cnuiSXMvSRZZlCXTAiVcMgCgK0fA4l2p0k3eE4qwIUVSWUvFS7+tKT+Ptef7A0auB6fOat/8rzf+8XDtWb2JncXMs+FKESeh6f/uu3Z/+2XY8vvuM9nP/il/mZT/4rc9dckb0fifBV3Gks0Dm/grtjDrA3pi9bQsyKRDbUDB1Jlpi+4sSh205F13jc9z6Pb/3Vn8yo96Hv0z67kiFMAleghw5KVHEUZUw8ECYjuUqJ/tom73j1r+9aFCetlTd88/fzGxc+LX6WaqWHfkA9NcqeiDQdpEComSbVue1dURxFOMMxZqmIZhp0l9fYOn0eb2xRmpkmWt1A1TTBMg4j+mnLBcBzPMZbHfKRRK5cZOqKY1RmpkWFXK1k51usVzPjBMUQg+0kDKnMieF8mBpATI419H2cwUiQgcrFbBcXhSHuSAweoyDE7o+IglAMmS2H0VabytwM/TXRIjSKpthltLuiHZn2fiM/wNhhKh6lZhNRFAlDj0iwqTVTDPVl0ySOIobrWxlvIwpD+mubNE8tIcsylflpeivreGM/lYeWxTxE16BUJPQDnN4A1TBQDZ3KzNEEor0hdGOiTNNcz+d2DVvFkPOAF+6452RZUORJ8dtREIi2zw4uymB9i0Kjxmhji0hXMSqlTHytUswxVSsJ2dyCie36zDQqLG90cHyfMIqZn6pQzJusbvXIpcYjYRRj6BqKLKPIkmCkHhIPj4qhLFOZmcqGT4HrEnj+LqPb+rH5TJtCkiTytQpREGVTcFmWGW21CTyfyvy0oNmbera9ieOY3sq6IIIoClanf0leiyA8NhPEVks1DMpz02yOd0N8hN9lsOvzHuhgaeu+c4dfI0Wmv7bJzBUnd/3cGY1Zv/M+Qt+nNNVg+vITPOKbvo4v/uO7D0SoHHiThhGdC2t84Z//k2f82EsBMcAbbrQgSVBUleriLN0La7z9x3/t0GNUDYMn/dB3A9tDWEXTqC/NM2p1+aaf+1He+/o37kqqkixjFAo88Qe/a5crkKrrVOamGG60RILWtAzGuDPs7oDW6fOAqOZLM1OiqtN1vvCP/3Gg/g4IUa873vcxbnjus7D7Q+JISBNMdnv91Y2sZVJo1I72z5QkmKjPqQqyqjJ18hj5SgVJkdk8fZ7GsXlCz8Msl/Atm0Kzjm/ZdM4tC1ni43nMchE5kclXKxgHtOImrbPyzFTmrSoG9qvZFqIyP4Oqa3TOr4oWCWD3hlmvXywKyQ5p2yRbpLV8DndkUZ4V1P6stZHq4Oeq5QwOWpnfPXScKIpOYLSSIpP48a6tzaRynzwXE62WSaGlGQbNk0tEgXiO2udWdvmsmuWS+I5yppDJ8H3idE5z0LM2MYoQmPR1xq0O9mBMsVoiV68iAfWlbay7rCrkqiWc3kBId6T9fEgXelnKirI4ihi1u4y3Oow7fRpp+1DPC6SQbzsUdZW+ZSMPx5jNOvXpBrqmpgNUhdlmlY3OAMsWtnH5nMFmu0erJ4AKxZxBd2Sj6cJJbKpWQlFk5qdq/+/1xHdG4HkZy1HbQcGF3XjJXKWMbzmZg051foaF669m3BIStFoq/p9ReB13lyiRogtlvr0O9IHjCmiWpmZf7kQQH8B3HLEd3FPplqbq9JbXsy2/WSoeWjXHcSwewlRLYnKM5dmpXWiXnRH6AeW08glcN5N5vXDrHciKjG6adM+vkIQh13/bM6nMT9M5u5JpUINYaPZ6XGbH5AV86s3/zDN+7KVEQbCrYgpcj8H6Fh/547fser+98YSXfgeX3/wYoSS5o8LRDIP64hzP/sUfp3nqOO96ze/QX90kSWKuevoTec6v/2/MQmGXXyhArlzCKOQFWURV90HOPMvG7Q/RTwhnKGckTIMnCXfr3rOHtpVC16NzbgVV1/cJb41bHaIgxCwVBXGl3RUWgYcMPGVZpjRVZ7jRBgmcfp/pK06iGgbD9U2sVpdCtUxveQOjNKI83aDUqGHkTNyxhaNJ6LkcSZzQXxcorEKzjmZolKYa+4aCsiyLyjaO6a1cyCC2geexfud96MU8SRRvLzyuh90fZIiSYqPGqN0Vcg+jMZV5MTAO/QBJFfo2kwQ7YUZLkjBFVmYOhsgquoasirmT0x8ShRFmUXh7TgaNiqYhKXLWMgk9H3UPu1SWZeT073PlInZvmFHtJUgtFjVGrU7WMpNV0U7bec/ZAzE0DlxP6C3FIYkkY3cHqIpMrlZBTjH5tR0zkPK00L0JPT9VOtXonFvJZl6FepXSVIPRVjtr70QbAb21TRpL88QpuUk1dKZn6uTtPNbYQSdhrlnZlc8URWZhuiZ4KmHE2bU2U40yhq6z0epjmjpXn5hjqlbC0LXMrSifMzi5MM1trdUDv4uHNYlP1OIkWSKJEzRTsMIOImzIKSstTCuSSTKsH5snCkPu+cinuf/jn+PYTY9g9qrL9n+YJO3rfw632ti9QYYNr8xPkysVGW11UHQ9E0ByhiPCPUgOzTBonFgU8ChJ2rcATSIKQ3orG4Seh4TwPZz0M5/5v1/GO37ytftaKpKicNlTHoPV7dNOqfyFWkX04bc6zF55ChAiRJ3lNWavvZKf+fQ7+dsf+Cnu/OAnUHSNOAi58ulP5J6PfZbwAHINQOA46TFGJGxXTJopSBHnvvAVokMWAaOY59pnPZXq/G79d9FqELowWj7H477nuTz2Rd+GOxwR+AL3DAgd+QNkcGVF2VdlTQZzvdUNMZxMW0mqnuLO0+Q1f92VaDnzwP69ahpMXX78wHMRBgY7hMEkaZ8N394o1KpohoHveoINXBTWf+0z58nXy0Sez6gl4KCzV50kjmMUXViZLa+sCqEwQ8cbjxlsttDyOZI4pre8fqgd30RqVlcF3X642cbpj/BsG3cgjBoOGuIVm3VxXVwPPZ8j9ALc0Vgs0LJE++yy0NROkswT8yhmNIjvqX5snnGui2oY+LaNrKr4roc7EuSvidiWIAP52Q5vojYYen4mqDZBdiVJgjscoyhKKlugZVaKZkmIRU3QTxNCme+4DNdb6MV8Sg4bYJaK6KaBK0tEUZRCX01h1rIjJEnaVdi1z60IZFwqPmZ3+xjFfGbQAlBdmKF7YR0r/V15bhq3P0SSJEqFHDlVtKEOI55JkoSfzh40RaVRLVIp5bEdjxMLB0Nj/5/z2JzEqNUVWt9psnRHgpl3mHuNJEn7es73/Pen+asXvILIF/oIcRhx2ZMfzcvf/kZUTcOz7KzKKM9uV2ATyJdZKgr1OU1luNHCLBb2aYKLJH+A3vKOnuRhYXX7xGGY7QjswUjYZpVLPPllL+Sej3yaL7/7w9m2Vc/nKM02eer/ejGBF+AMhiRxTBQU0As5bvu393P6U7fgjiwaxxe44bnPYvbqK6gtzPCK//hrrG6fwUaL2uIssqLwf5qPOvhaysKfc9zpkSsXIdnWoAg9XzBlTyxy/vNfPqS3LVGdn87kRMVuRkvp1x6SLBF3+sRzUxRqVXKVMjkgaTawB0MBE3M9oVypaVi9Ab5lU5nbL1gl5iM2kiTj9UeMNtvZLkYvmHiWjVHI88SXfifv/tU/PPB8NdPg+m/9+gN/p6dtBaOQ3ybkXML8ZGLoragK63feR+B6tM+vM3/NFRiFnDAd6Q2JAqH7ImRpBW1ekgRCSy/k0Q1D2Kmlw9zMEnBPyKqKoikZvM637LQ3ruAMx6zcdidLj3qEkL7d04qatI2M9JiNQp7+2gaqbmz7ztoO9WNzqIZxScxXVdczB6De8jp6XlT+O12qNNOkeXJpl9hWHMf0VzfEApwuxrXFWRRV3dVmncQEtZWxHfXd0ryhL8g/cgr/nHgLFOtVSBI8ywUkAsehcoBi486IgiBDmUmSJJzDUkjqtmS0YAfXj81vywtbjmCr+j4SErNXn9p9DnGc8Tg000CRZZJ4tzHEpdL5930PD+pVD1Ek8QEGCg+Apt4+t8wbn/NDQh5zR9z/iS/w1y/6cV75n28Rsq9hSKFZ2135JQm3/dsH+MgfvwWrOyCJE04+/ka+729+T+Bet9rohbzQd+kOcAMHZzQW27sHwIqbEDEmoagq0URZTpb5oX98A2c+cwsf+7O/J/R9rn7mzZx6/I0MNttopo4kKYKh1xvwzp/6Dc596SuZENfaV+9h7c776Jxb4Qnf93yufdZTKNSrFOrV9BQTnvajL+Fjf/53+xAwSRxz5jO38POLT+CFb/x1bnrBt+zuiS/M8PQfeylfffd/HTh8LdQrnHj8o+ivbWa+id7Yxh4MBVwsTlB1FUli18DO6g0YbbbQcjlCz2ft9nt4z6//CWc/d1s2gP7WX/0JnvaK72PiPmT1+uQrZSGn2qwz3Opk8sWaOUN3eY1Silx4xb+/mT9/7ssB0X4RZC6dV3/4bbu+h4wUJQkXJIFUsJCA0kzjknVIojBk1OqQr1cJPY9CtUjgOGmPWyA8xu0eZrmInhMSw4qiUJqeQi/ksO8+jT0coZcLogrmcFCPJElUF2YZrG4Kj8sUwVWsV9FzJqN2l8B1mbvm8n3w1OFWG6c/zHq/cZDiv9MCarJwSpJ0SQl8Z/RW1hmsbWbmK3Ec43sujeOL5FMqvbRjYXaGo5SoJJ5Hz7IFumeqceD7K5pKEidZEg0dN0OpQWoWkc5XzFIRaaMltJmGY7RCnkKtTKFWIV+vXHQmZqa6Nnohn30fqi5o/v3VTTHEj2PK081dUMriVF3IQ7sucRSzdf95Zq86lZGGestrWatP0VSq8zOojsXy2fMohk5pqsGJ4wdLVVwsHtYknquWGG620XO5bLWdkDYuJf7rj95CeIAwTOj53Pexz9E9v8rUZQdvoT/5pn/kP3/tj3clt9Of+iK/8/jn8Uu3f4DSTJPeyjrjdo/idIPhl85z5tNforowS2W2ecmkHr2QZ9zqoGjF1KEoRN9xjpIkcdmTHsPSTY+gs7xO4Li4lp0iE2xkRcIdWdz/qc+z+pW7dispAkQxn//7d/HFt7+Hy554E6/49zeRS2+u0PN4yg+/EC2n89E3vDUzXZ7ExDvzn171yyzecC3Hbrx2Vz/68ic/hq//yR/iw3/wZkLfJ4nijNH6iv94M5Hv4422dVMC36d1/3kWrr8KRVHxbIdxe/dAxur2MdLJ/WhzwF8+/0cz4k5EQOC4vPNnfgtvbPN1r/p+OhdW6a9s4vRHVOemydXLVKt1QKLQ3HY+H3f6mJUSl938GF5//lN8+d8/SH91k5krT3LD875h1w7O6vWz4yrUKsKdaHHukvR59kaQij6ZxQJJIU/zxDHGnS5RqnfeOLWIjJQt/KquoeZM9EKO1n1ncUdjrM6AwfoWxWaN+uIc8hFtDM0waJw8RsX3SSSwWl1CzyeOImauPImeYqMhnfm4HlEQYHcH5Crie9JMQzByU30hoUEfplotDywl+LaTIkiEjnz7zDJICVNXnGS00SIJo33PSpyyeyehpEPLo865sjDDcLMNcYxeyO9K+Ho+J7SG0pZGfWkBo5CDVLbXTFs7lxJGqYg9GDHcEgqKtVSSQ9EEoTBMwQx7F3m720cvFvBsF1VVGayJhD912XHckYVvu9n1922HzfvOUjJNiicXCLwAKQrQlIfZ2efBRL5aAUkS2symsPo6qA/XOnOBr7z7wyRRzLXf9DTmr72CwPO496OfPbR3qegaK1+568AkHnge//4Lv3+g8p83tvjIH72F7/jtn8cdWuTKZexeH0mS0fMmUurcM9lKXywKtYqgeveHwkBgtnng6xRNQ1EVgjjGHYxwRzaB50Eshjtffc/HjsSCx37A2c/eyptf+GP82Pt2aL1LEl/3yu/n2m94Kn/4zBdnjM5d18Px+ODv/SXf9Ye/zEff+Lfc9YFPkKuWuPmHv4fn/NpPctN3fjOffPM/MVjb4vKbH8sTf+AFFFLnoV1wLEX0AgPXJ1HFwEffN6ze3m194k3/mCkF7gzfdnjPa/+ER3zL0zHLJaoLM4xbHdrnVvBsi+Lll+FZbvZgTnRkeitrxIGgZF/7jU8TrZk9D29vbZO1r96NljPJ18qMWqFwlSmXjuwBC6bwUMgbG8YuU+/J5lGSJMqzU2imQXGqjp4zMSslMShLq8goEIqHuXQ2Ul2co3d+lSiOMQo59EIepzc4skiYtBVnrjjBiu0hayrlRlW0EsrF7HgHa5u4Y4s4ihmsb6LnzewcJUmiPD/FuNUVUN505rRzl7lzt3JYhL5PrlbFGQwzQlGuWqLUqAlWbG9AYY/8xEQhMZ54vbou+frRbY5cqZiZhu/9TidaQ/lqWewk9W2noigML3l37wxH9Ne2BMjC0DELuYxEBqJ9dJjJRRzFuMNRZiYSRRGjVk88C6l8gZYzxCxPUQRx6jLxHRuGLvRm/OAB7fKz43rAr3gIQ5IkCtXKoYyqJEn4x1f+Ep/5v+8QSLk45t9/8fd45Ld+Pd/2uv9DeabJwfNa8drDtmcrt93JYVi00A+49Z0f4Dt+++eRJIjjhMD1UXRN3AtJgu/6WGn1cVTVtnX/ObbuO0v9+CKzV18mkk0YCkjVDiw2CHf7ZGKikM+h5/PoeRMtFXU6RHd+97F7Pvd+9LO0zy4LzXVdz8yB2+eWjyTfnPvcbfzKVc8g9Pys33jm01/i0295O6/4jzfzoj/dr0op5hliuKhoGpHr0Ty5SL5aJg4jtHyOQm03e6841aC/uomiKnz1ff994KICIMkKa7ffyxVPfTyFelW4I/WG5HIKhWadKBQqhoqhE3l+irBQMsSLOxrjDEdZBa4aOpHv07r/HIqqouVM7P5QVKX94UW32aPNFs5wLIZ41rZSn57PoaZ4bilN0jNXndqFVKrMTbN1/zmc/kj0wUv5TNgsTAfAZgpF03ImnuVQvARItFkssnjD1Yy22sRRTL5WyVpp3tjKCFiT69FdXqU6P5fefwqe5aCaJnrOwCgVMzeuibTsuNvfhlym77s3hPWZQIt5wxGe5dA4Pi8Erg7hLhiFPJX5aaGQmCRCPuISoL8TuPFhsXO3NWGETjDixUbtyIUxSRKGm22M4rZnpjsck69XL4n7kauW6JxfQdFVQl/g6gNXsHtVXWN0z2na51eZOrVE6HnkyuVMswVSeYc9D3mSJCmowkPRDy8wHnaI4VHx8b94G59967/uqUADbvv3D6KZBo/6jm/ivo9//sCK2iwVOPWk/QLqIAZEh5mhAtmFLTbrdJfXSQB3OEKen8fq9ISynCTaFfWlhX2r53CrzV8+/0e5cMtXxYMaBMxceYrvffNvYRaLSBIZ02uyCIjhjAIpvEtRVUErLhYyx/pLCdXQWbvjXponjwl0wOIcdm/AnR/85JE0+O6F9ayvOAnPsrnv45/jc3//bzzppS/Yfx0nhI3VDXzHoTw/TfXYHONWD0lPRIU4OyWm/L2BuMFdD1mWsLo9JOkog1lhrze50VVdx6wUCS906V1IvStVGSUBs1JCMXQhiJaGJMvCICQlDKmGQRiGjNtdfNfDGFuQ0sbjKEbR1ENZkAKaZ2U9XFXXMnSHquvUlubFcDeMMIqFfVvtCTuxMi8s2Vp33S0GyWGUko58ojCgNN0k8oOsmp7ExOBYUZV9vW6zWDgQCLBX07syO407ttByBomU4A7GdM6uEKYG5PXji9QW55BloeMy2NgSUEFJYrixhaKpB6KJ9HxOWCD2hsiaRmW2iaLr+I4rLAjnDvbBvRSFxK8l7K5wZ5pU76NWFy1nHmnWvLfKT2AfmuWwyFcrNC87zta9Z9FyMma1TNTpo+oa43YXSZbpnl1BliTmrrsSPWfSX1nHT01NzEpxX0G4a46RuoYdFA+Z2/0Dif7axpH440l84Lf+/MChWuwHfOlf3sPUFSd45LO/TpgGp6GaBkapwP96518e2gc7duO1aIdYi2mmwRNe8u2AuEEbx+eZOnWMwnSdRALfdqkdX6A03SSO4sySLTu2OOYPvu6FnP3srQSOizMYEdguq1+5mzd+yw8IDZZUVsDubROIjEKe0PczjXNnNEaSJfyxxZu/65V0Lxy259hzbaJol69mEsec/uytfOlf33vk6/Ym8En4lsNH//StB/5uousdByGSJAtd8VKRqVPHqJ9YpH58kc+89V/52YXH8dOzj+Enazfw1y/6MdbvOo3dG3Ljc5+167vbGXouxzXf8BSiMEiVBCNkVcnEpoxSAWIozzYpTzcxy0UCz8sIH3Z/QBIl5MqlTDeld36VXK1CoVrG6vbYuPcMhXqVXLlI68wyneW1Q53fJ++7HTvwv6pKoSbwxDsTeBQEqWriCM00UrSGQRLHAtaXJIRhgFksoOdyQlTN0HcRjaxen/a5Vfqrm7TPrWL1D9eV3hmaaRKFUcZuDV2X2sIMldlp4iAiCiNREc9MkSRg9wcZu9UeDEWffmOLwfom404/42fsDUmSKE83mTp1jMaJRY4/9gZqi7Pkq2Uaxxe+ZoOOBxue5RAGIf1VMXSNw5DA8wlcF99x933HkiQJL9uxJZA/jrtL8fBiIUkSjWPzXPbEm2ieXKQ8Vac6P407HJEkYtFavOEaytPChMbI52icWETVFJBE5d4+t0LgCpRaHEU4/WE6mDeyAuKgeFgq8cDxjsTDTqK3sn74e/gBoevx5Je/iOu+5eu5/T0fwe72ufwpj+VpP/qSA9l+k5AVhRf80a/wdy/7WWJ/x2KiyJRnp3jaK16S/WgiotO8/ATNSg3fdrPVfKKJvDPu/q9P0Vte39ern5jt3vG+j3Hj875BbMt34Jlz5RJRGGF1ehQbVSRFIVcpcfpTt4hEeYTb9c4oNuscf8z14jOThN7qJp/9m38hPsIZ5GIx2GgzSP0OJ7oXIHqIcRBkN5jvuFi9AeXpJgrw/t98I+993W5pgbs/8hnO33onP/Ivf8Z13/R13PquD9Bf2djVVlFMnRe+8bWZ/s2EXr51/7lsWynLMkiimsUQD0nkB2JhlCRylTKKshv5JCkSZrGEJ8uYhSLECYV6jXFb2I9duOWrDFY2KDRrVGa3t/g7CTOKpgkxp0rp0P5okiRpFSWIXKEvhocaELouo802LCxiFHIENjSPL2bWaxNndkiRL1sdzFIhg6GNtzqpzvbRw1c9Z1JdmGG0JRQvi826UBJMErH87NWBl7ad4n3LIfT9bDEZtTr43tFqlIqmMTmi/8kK+6CYwFy9VMs/Xy0TRSH95XXydQEHHF5YAylBUbXUitDY90yVphooioI7tjHyJsVm/QEjdXbOypzRmOVbbyeJEoxSkWIzFW6zbKFaObJxBkNqqRdr6AcMNto0T2yLvl2KPs7DksQnbuiH4WFBJIR8vYK1B90wCVXVKM40Ga5tceqJN/KE733ekc7ae+MJ3/vtRL7Ph37vTWzeewbNNLnxed/Ad/7BL2bojp2MzjgIyVcreCM7HZaIHnRldjem9dznv3yobnZgu5z93K3c+LxvEHC0HX3G0PeRFTmjgk8e0gtf/OrBdPo9oeVMVF3jf/3bX2ZfvDDW8C+5FXNYzFx5guVbb6c43aQ8LTTfZVkW7YudiUCWsx2WO7Z4z2vfsJ94kyR4Y4svvuM9XPP0J/E9f/rrfOZv3sld//VJAtdj8fqreeorXkJ5uk7nwiqyJGFWy5Snm2g5M/MEFYw6MpRD6HkYBUFlV3XhrtO9sIqSDs+SKCJXLFKcrlMIyiiaijsqsnHHvXzhn/+TC7feTq5S4rHf/W088Qe/k/7aFqquZe2LQqOW9d8VXTuyMnJH44yDAGB3BbmGtK0kybLAx6sqiWni7HBG2nWpdshOTK5vkoifcwkImoMULyVJwqyUsAdD3KGFrCkomoqsahkyzCjkhQBZet/p+RxG/msXnDpI+e+hCKvTY9zuoui6gF5aNrIiY6Ra60mSEthGDo3joi/uWfaBmkC5SmnfIPbBRq5UZP6RVzNc2yRXFQYVgetlpKskibE7Qk9eKFkqYieQzsxy1fIuWOhh8TUl8Q996EO8//3v5/d///cBuO2223j961+PoijcfPPNvOpVrzr0tQc18icxmRI//iXfwcf+7O+2zXzTkHWNRzz769i46z40Xae7vIHdGXDicTdcciKXJIkrnvI4rnrGk0UPWlPxLTu76ZMkYbixhT0YIcsyw9VNpCuuoLIwg9XpA1BdnN2HNMnXKqipRde+z0xRCe5YEEvyaRIPPI/uhTVIEpJE9LXrx+YEiqFSOvRLlGTRX6vMTXPV05/IzS974a7hzYQgcfwxj+Tc52/bRZC41FB0nSf/0AspNGpiym47BI7YjeiFPKN2j0gNIZ0RFJqiejv72VsPhavFfsDt//nfXPesp2KUCjz7l17FU3/0xVQXZjFLBQZrm1i9QYYbt3sDcqUilZkm8t1KCpVMuP09/81H3/hWOhfWKE01ePLLv5vHvfC5mJWSsI0b21j9IYV6lcrcNIqhC22WOKE6P8sd7/so//zqX8t2Y3a7x4d+/684//kv851//EsMNlJd+1S+NVcqwsXnb4IbsOPc9WIBvVQgVy7ijCyIY+z+gEK9ShJFKHtIHqKd0YNEwDYlR0EzDUFc07UHDAPcG6VU+XOwvkXguBQaVapzM9kAb+KKo+oaJAlRGB/YD38g4dsOvbVNiGO0nCm+j4ucRxwJo+w4FaE76BiSJGGcwlZF4jWEaJgu/HMnAILhxtZuWOOeZ8odWwzWt4S3qapQXZh9QGJ2h0WhWoE4YbDRAoRdom8LoTBZUQQEudsnX6vQW14HEtpnl7NrpJlGOtgsw7h/4Gc86Lvhda97HZ/85Ce55pprsp/9yq/8Cm94wxs4duwYP/zDP8ydd97Jtdfud3tvn71A87Ljh16k4VYHo5Dj63/iB1j98l2c/syXCNOkaBQLzFx9GU9++XcLanfOREJ8kTupuCCq+XGrQxzF5KqlXaQTAGSZJI5QUgp/Em/Lpwaut6tCUgyN4UaL5qmlQ28mezDk8qc+bl+LZRKqpvJ1r/p+pk4t7bqBrU5PwJrSSsgbW7hji3ylzKO/+1t5z6//MdEBC7GiabzgD3+Zy5706APJKbIs2kM3fsc38bE/f9uBx7T7/VSSBPS8oCdLisyzf+nHmLny5C642eS/9ZxJbXFWYK7jhPKckC1IkgTfcXZJl+4NvZhj5upTaLrAF49bHeI4NZ0IgiyBTD4zTmU/S/PTTJ06xt//yGu45Z//M9v1DNe3+PDvvImVW+/iaa94CXPXXk5tYRZ3ZFGoV7LFzSjkU2eZiH/7ud/a3U5DqPPd98nP85V3fYhHfMvTyVXKjNs9gUaZvzTjBdXQxdAzHaqGvk+xWccZjMmVCmj5HMMNIflQW5zbtSObSLfqKXtUsgWiqX1W6PcUmnWxABziKH8pIcsypWY90+zfG7lyiXguYtzpp9DDB6eVP4koCOitrAu7QlXFd1wG61vZszqZN+xsXewSsFNVrO6AeLZ5aeedJORrFcatbiYcZhQLxEmcieqFnretQR+Gu5QJQ89nsLZF8+Sxoz7l0s8/DEVrT0LM0JIEioV0mD7FaKvDYLNN6Hk0Ti2hqiq+7TBud6nMTsNFxgoPOonfdNNNPPOZz+Sf//mfARiPx/i+z9KScH+/+eab+fSnP31gEtdyh2OsJ5KoE9LFS//2D7jv45/jvo9/HkmSuPHbv5Grv/7JnPnsl+icWxEIBwnsTg97MMxujND36S2vCcyoqog+JGQ3QRSGBK5Hb3kDWYZcvSIMj9NEulfwSlIUoiMskiaTZCOf49m/8Ere95t/nmlDIAkLuW/+hVcdqOsi6NjbN7Aky9nnT51a4pk/9XI+/Adv3mUwrJkmT33F91Kaqh+ZLHOlIicfdwM/8i9v5E0v+nEC29mF4tgZsqLw6o/8A1a7h5Y3qR2bxxmMhL6MqlKcroMk7RoKH4SOsDo9CvXaofK4ej7HE14s2l9aTgg76aUiURCkvpVyRkKZWPRNeuGSJLF1/3m++E/v3rfbCVyXOz/4cR7xTU9l/ror0s8y8cZ2BjedONLf/eFPHnrNQtfjq+/9b5740u8EUjTK2MrIQFEosN6HVZJmqUjY8LF7A5IEcuUCet7EancxS0UKMw2qs7N4Y4vqwsyu3aM3tgTtPZVu9SwLd9MSO5EFoVMz2uygmeZD6m4PqfHDQPAZ8rUKM1/DQrEzJg73k+ulp62aiQHMYKOVkXgm/qih5+8RsNMYt3v7CrGJUNeo3UXVdUGmy+eEmFo+h2c7SJKEUcjjjMaMW12SVNjKSOWlJ/OrSZtHTXHbD4Xxtm872N1+1qL1bQe7PxQa7IpQK6wuTDPuDoRJSatLaaaZavhvzyEOKwzhEpL4O97xDt761t3ohN/4jd/g2c9+Np/73Oeyn43HY4rF7Qq1UCiwvLx84HuutbdoO2Py9YOXGLvbx+2PUE1DPDiNEje+8kXZRb7nnntodVps3H8Gs1aGWAj3RKfPMFJE9eaOLOxOL1ss4jCClRXKCzPEUUT77jPY/RFy6iqTjIeMVdD63ezvh6sbSIqCrCpY/SErqko3OAAtE0UMLqyhpQPP6ac9hm+o/B/ue98n6J5ZprI0x03f/x0sPOYR3HXXXfte740trK0OqmkIenEQUlqYRt1Yx+72aT7xBp72c/+L29/5foZrW1TmZrjquc+gtDTP7Z//EuXVFcoLsxk0MvR9/LE4TqOYF1oTUxWe+ze/ycYX7uATv/sm2JPIFV1j7qbrcIsGamUeJImOZ+N4Nl7gEnsxw1ZIrlahe//9B35vO69FFEbc+OJv49a///dd1a6iqeSn68w/84mcPXcOf2wR+D65cgmtkCNuC2VAlITenXchyzL5Ro3eGXFDu67Lh/72nwkOaQ1Fns8t7/8I+euEdoWQPVBpubvRFafvulcYVxwSo96Ae++9N9t5BLbD5qCH3ellO5HCVAO9cHiFOhmcyX2PqLXFIK1GBxstRqubhJ5PxxlTmG5kicnuDvBGY1TTYLzZxh/bqbyAib68TK5WwbMsiv1OxgB8KMK3HMab7ZQPkZBEEeX5mUPxya7rHngvHxSh7zNa2UItCOJXFAgCTtsZM1jdQDVNZEUmdDzU+w2K0w1C1xOiVun1FQgbj07g7NsNJUkidGRc8V0bYYHNuw9G8Uy+u17bw/M87rrrLqIgZLiyjpIeR+QHkCR0o6OHuZcS+/JQFBMHAXlqKYM7wEvz0HizTbK5iblaEjvUQi67b8etLnLh4EX7okn8BS94AS94wX6M8N4oFotY1vYAzrIsyuWDp9Qnjx+nfICN0ySSJMHq9vHGNqqhZSJJO2Npdo7VagN3NE5FacrUFmczmUl3bNFf3chW8tDzkTWV+uIcvdUN5Fqf/IkTxElM6PoU6lXqS/O7BlbBlVcw2mwThSEX9HUe9cTHZTZqQvdFzbb6W1oOo1jIbrBjs3M8+0dfesk9emc4wu4NkRQx9NJzJr7j0pNXMU6e4robr+frvvf5IImKYfPeM+TKZYpNUfGqpkFtYVb018+vIhWr4lrGMY3jC8RRzBdaXb75lS/lyhsewdt+9DWErp+501/25Mfw/N/7BXRDCOfv9be81IjCkLaeR1EVZqp1Tl53FR9949/RPnsBo1jgyT/4XXzrr76as5+9lQ/8+p+wftf9FBoVbvqOZ/PIb30G09deTmVmalunes8De8dXb8eUFDhiVzSzuMjS7ByJJCErMvVj8/u+h5lihQ++5vcPfL2WM7npOc9kaVZQ4OMgIF+vCo2YabFYxlFE4Lg097TGjorx/AKb956hY7ucvOpKiqlsQGV+JmvbTbwtXcui70bkT5xEUaRUDG2EJhsEWkyjVGX6shMPaJh/VPRW14kaUxmkzrNsis3aoe2Lu+66a1cr9WJhLfUZbXWRpFR8bXGWKIzo54rZTk604VxmLj8h2in1NSI/QEqdh0rTzUNJRw8mdp6Dc/Jkph30UPbEA9elfW4VoyDACu7IotioZu29UauDOxyj5UysRSG/oOoazZPHBDIrLVw3tXOsDA4GKDxk6JRisYimaVy4cIFjx47xyU9+8tDBZq5cPJKhJUmSYFgdIcxvFgtU56eJQlHFSLDr7ydQH2c4SuFoEpU5gSTxLZt8rUrouEKiM/Gy3tvO0AyD+tICAG3PFmJGvT7D9RaSLKPnjIyqXKhXhZ6x5xO6LoXmwRICh0WuXNp3TZI4JpEmDuJJJqlZPzZH/dh8xk4Usp6ianD6w0ybHcRcwBmMKE01yDdr+LbDsUddy6989YNs3nMaqztg/hFXISvytp644zLYaO2aL1xqCCutAs5wTKFRY+GRV/GCP/plKnMzlKbqlGeb/NcfvYX3/vqfZP1stz/kv9/wN6zfcS/Pff1Pk0u12Xf237NKtd3jsic9mk/8xdsOHB4bhTyPfeFzqC3NE4URmnEwVbp+bJ4bn/sNwqB65/tIEppp8Mz/8zJU0xBUaF0gN+zeED0n7pEJI9GzhdjVUYl8AoELPA9khVy9Qm1hFi1dqKOdOxVVpX58AXswJE51R5IkoX12hf7qOlOXn6S2OIeWMxlutLL782uNndc6PeiLvib0fQbrW6L1ZRqUpuqHKiAWatVsHpGZOrgu7FDyi/wAbQeEtLY4h90fpmYNZczJnOAhQI7sjVypiJHPHapl/2BDM02q89MMU6hnrlzIAA2we35SqIlWUbFZozTV2HWeh/Fa4CGGGP7ar/0aP/VTP0UURdx8883ccMMNB/7dpfoDOsORqH40jcIeB21F02gsLeAMBXTLKBV2rZwT/XHfdmAi/5i+XtF1zLKMHQQ4Y4vAdpm6/PhFe4zu2GL1trsEqkaWMYv5VJFslnytwmCjTeR6aRsoznSVH2womoqEQChY7T5Wp4tRyDFYk4mjBC3FbIeuh1G8OATMLBeZvvyEqDZSn0qY6LqvZau+rKlYnT6FdEfwQB+a0swUsq4z3uqALFNoVCk0qyRxzIVbb+fdv/KH+xBHoetx1399isd+7/OYukzMVSZCVUmSUKxXKTRqBK7LNU+8iaXHPJLzn//yLsSNZhocu+kRXPakRzNY3xLuLAiFuYMqyu9/6+/xT6/8ZT7/tndlD9PU5Sd42T/96T7JBuE2pGa9em9s0b2wzrjVJfR96scXmDq5dCB8zu4PGG620UxT0N3746xtEfn+PsKTrCgU6zXilDcgqypGMcfs1ZcJhnB6H18K9HQycA9sB1XXydcqBx5joV6le2EN3xa64qLNeDgiJUkHjyChmgbtcyu0T5+nMj9DZW4ao1jYZ4SydzHVTJNisyYGqBJIimAA77oOjRrjbh+rJVx1NNPYp/GyMzzLzgq3fK3ygHYqB2nZPxQxIZwdpPsymZ9Y3T4gUagJM+m9z1xpugFbB/NmvqYk/vjHP57HP/7x2b9vvPFG3v72tx/xChGXkhSsnkCbKLqOMxjjDIZMnTq+6yIrmnZktS6nmsZ7ozI3RW95HbNSQgsiSlfWxBR4R8RxLHrzYxvNFAOTwfomURhSbNRE2+DsCv21TULXxygXUXWVYgrUj6P9TkIPNITO8hyb959j1OpQnW2Sq5axByMCL4A+GbpjknRylRJ2fwRpdRmH4S4a94GGG6oCCURRROT7tM+vCgnOCzpmqXggKmPCihR6IDKl6W22oiRJ+JZNd3Wd3vm1zM9x+ooT3PX+Txz6/Ueezx3v+yg3ffs3CpTGRjs79nG7i6ypKLpO5Af8wN/8Hu953Rv5wj/9B6TY4yf+wHfy/N/9BcZtoeyXUa4PGQRqhsFL3vzbfMfv/Dwbd5+mOFXfZ4c3CUmSqCzMMFjdxB1Z9NfWUTQB4TSKeTpnlpFlhebJY/vOz+oOMIoF0TqZnUK5S2Xc7qGnhJLD9PNLU42MyVuaqjPc7GSSrn7qZnWxGLe7mV+sbzl4ln2g8YpmmtSPLwjddlnCLBWP3F0IB3shMzBY30JVFCJJQjF0esvrYpGK431GKHuj2Kxjlou7K3RElT9ud8Wz3x9QWZxDST0tx60Olbn9glnu2KK3soFm6CRxjDMc0zyx+KDagg91HKb7MpFCLtSru4a/e+Mw7gn8P6ydYqc44eGGYCv6jiM89/YIxj+YEN5+x1JZSfnA1VoIHgmtCbs/ZPmztxHNz2UrqjsYY/eHTF2+hKQo9FbW/0dWcUH9XxBCWDmT/krqBSnLKJUSlfmZXUlAM00ax+cz2zezUso0jUPfF+zBPeer6jq5eoW12+8RmsnAsZseQa5cyphwej4n+neDkdB1MfXMLDqOYroX1micWEAzDGHvttFCN0SSkmSJcXdAqT/Ec5wjJ+0SEppp4gzbqPo2e1G4xzjkm1XiOCKJYr7xZ/8Xz/2Nn0JCVJKT8xputHY59Ujy0U49hXqVk0941EWLi4kMbJjS6a3uIBtYTQhsO53Ss3OSyNoTYlBbZeqyJYxC/sh7ZoKqmDCEpRTbLY5F30c02xkT2J7VG2xjqA1daL6kRsZREGT3kpYz0QzjkvvAsrztLevbjtjJpPj44XAk4KBpYbHTCOWg2Hs/CkXKdUiEho49GKEV8pSadVEQ7GmjuWOLcbtHf20Dsyjw+KHnEVgu9mB0KJTya42JO5Ekifvza2nzHHQf+I7LuNODOMYZHr7reliSuNXr4w4tFE2h0KgdeOPEYcTGfedQVVWw8CKd0UYrY7p9rSE0gQ9+gOKUIm8U83z6LW/nQ7/7lwRhSBLHlJoNvvk1ryBXraDnTaqzU8Kf0xBIGs+yBTzM9ykfQf1/QJEkeGMbb2QRBoEYfjbraIaO3R/uq+TEtl1luNmmd2EtxcPH9M6tsp6oFJs1qgtzu2660HGFdrKqISsSdnewTfdOdxVOfyT8D6OIzXvOCsGkdAs6SQiaYYiEFcfIskShUWW42SbyXKzBiKmTS4fqtOiFPNc/R7jvKJowz5ikwzgMUbUCqq7TPHEsM9c9qMrSTB3PclJW3OEswdD3GWy2Mw/VfK1CaaZ5pN6HJElouo6ezzFY3yIpCFs1wR5VD1SKzDdqdE5fEEQdRUE1jKwyv1hY3T6tMxcAaJxYZPryE9n5HCjWFceMWh2xiEsS3tjeJfo0eUXguoJghkj4ej6XMXEvJWRVoTjdYLQper3OYEz9+LzAYLs+hfr2DlkQa462u9sZoednnqcgFkhnMKLYqKWww+373XdceisbmZ/ocKuN57hEni/EpcIQs5jfJxwGYrHYa4h8VERBwHCrQ+gJVcHID4gCYRyhFwuXzCO4lJgQAFVdQ5Jlxp0OFA9uDT0sAlidcysgQeD69JbXM5xz6Pv01zdpn1tmuNkWzL3+kNbp88iK2KodVcU9VDHxWfzMW97O+3/jz3BHFpHjEXsBg9UN3vG/X4c7spi+8iTGBFYpQf34AoVaBS1nUFmYfcAaEmt33MtffueP8pO16/np6Ufz9lf/Ghv3nWW40UaSoH1+ldFWm3ytTK5SEtjyQwZQEzaeUSxgd/qs3XEfTneIO7JYvf1eRq3tSfekojCLBQr1ikDfIAwFtinLFnohlykshp5H++wFeivCZiuJtjG1qiEUB13LRlIkVE3DKBdRZIl/+enXH6oMV55pcuPzvgEQxth63kw1JkQPOZ86kQtilHnoNrk41UhNNSxh09Ws7+MlxFFEd3mN0WYb33YI/QDfdhiut47cuk6itjhLcarOaLOdmvvmKE839hUYcRQJ/0VZyqQm8o3qpSXw3oCzn/syvu3i2y7nPv8VQcPeI2O8M+xuH2cwEqJapkEYBIzbwkzcHVvohTyqYQgf2VQlcyIQdinnvTMK1QqNEwssXH819aU5JARooHZiHqEBKO6tOAgekNmLtENzXsuZFOpVAtfDs2yh1b6jshYyyCqKplJq1omCiPaZ80LQqlwkXykzWNva9xlREDBa3aJ7YZXO2RUGm61DbAjJzqO3vJ6yJ3X6yxv0VzeFQXqpmN2nk3DHFu0zF9g6fR6r1z/yvQ8K33ZEl8DQUTT1wPbRJB6edoosZ/6U7tjKhlPdC2tMjFCd4ZjyTBPNMFJYknIkweLBxkTsf2Lsa6YwwXy9wgd/9y8zVbGdEfkBd77vv7n6GU/MNDHy1XJm5Ppg4uznb+MPn/EiAsfLSDIf/bO/4/P/8B/8xIf/nkKjhjO2ad13luF6G0lWUBSFysL+L3cCf5ugV8IgwB2PUUxVtD/iiO7qRjYBl9PvI/QD8jWh8TDYaCElQlpAMwxUTRWOLOl2NvR8oVUxthi3O0xddhy9kMcdW9z+3v/GHYyon1jEG9mEQUDz5DE+/Ptv2kVg2BWSxEv//g+zxDxBJ/iOm0rJ6pdcJSqqSn1pgSgIUkmF7WS/00c0DiOSOEHP5TJ9baNYIPC8izIUVV3n2A3X4p5aIooidMM48DXOcEToCXgciMGbv3W4EmEUBBlCYrC+iWpomOnQOo4j+qsbuxiee8MdWVnvX1YUirUKWj6Hljq5T+7Rg3xkdxYEk5mQbzuopkGxUTtwNzNpweQqZeFS1BUGKrEciWdDkijNNI+UgN0bqmGQq5RwBiMkRUHVdU489nrMQn6fDr8kS9nOTsuZlKYbJHFIvl7FLBVS20BrH6pl1OqQSOzQt+ljFguHHqczHNFbXUfPCRchYQrtZpK/iqpmu43AdemvbgoTCFkW0EVJekCKjjsJf8AuMbd91+uS3/UhjOFmi2K9Km6KlAYbuB5xFGOWCsSRkMlEEmy5IH3gduImH4pIkoTB2iae5aCoClZvkHk1Wu3eoaL2JAn3f+qLoq/u+Zmt3KT14AxHYuhTyF8yXfnvX/5z+7xC4yBk3O3x4d9/Eze94FuIw4DK3BRmOY/V6TH/iCsPlACQUlZiGARimy9LxF6IouupqJKMmrLFJjf2RBc8ShX3Tj3hUbt6mKWZJr3ldbyxxWirQ2VumtJ0Q4jfez5mpcTn/u6d/MMrfhElhd8lUczJJ93E9/7F69ELBe7/xOcPb6XkTYbr2xXTBL4WeH6mxS0/ANzuXjTETjicoqqCaJYkqKYQTRIGxnJm+Hypn3GxwXUc7tb1Fg97IOCGe1xonOGIwXpLdGRSVbud92ASJ1zMHUQ1BNNPTluFSRxTqFX2JSezUsp8ZCekp52yq4P1LfxUFdAdjgk9P1Pb23eOccxws83WfWcxywUh9hTGlOemMo/NBxLCIWkas1wSRCdDP7AdAgL54QwmVbCEUcjROH4MsygSuG876ClcdWeEXrALUjxhyB4UgesKj0wvRFZD+isbaIWccO1Jzz8Oo2zx9B0XWZG3Gar5PN7IekBJ3Ex30BkC6Yhr+LAk8SiIGGy2Msq2cDOxCTxXuMWYovpOkCjNTBFYNtVjc5e0midJQuiJNoAkS/g7aLd7H87Q84Rw/ETs3zSEGE09xbQewejTTXOf2/1OvQdJUbA6fSrz0xd90AfrW2zcffqQixVz6zvfz2VPfrSQ2tRUyk96DPlaFf2QG3uijbxy650kiRhoKnmdzkYHq9Yk36xmrQmhnijaE80Txw6llGuGQePEYqpXYxC4/rZdlSSx/KU7+IdX/CKB7bJTjeT+T3yBf/np3+Rbf+XVSPLhC3AUxcKuD/Ed9lc2SBA3c+j59Fc3aJx4cFoWSZLQXxN9b6NYwHc9Osvr6IbQZwl9H3c4pjhdJ18pPaCq8WIxsSJTdCGB6rsugeOkkhFih1FdmIUkYbghGIpySu0PHIckiRm3BYsYSbooLrw01aC7vJbuEIXg0s5CYkJUy1dFq8/tD5FVhfIOQlQUhnhjO+s9T1pTExOMnZEkCYP1Lfprm8RhhDu0kBLI12t4w/GuxBXHsRj+pjo4e/vQURDg2Q6yLAvlxIt8D3GqCpivlTNkh2YKI5H+2iZBqmx50ADYLBWIUjOZOIqI4+RQLLY7sjDyeSqzMla3J2QEbImZK08QpDvF0g7bRSFRvZ074jA8ktl7UMiKQv34Ar7tCFmKnMnKV7sH/u3DksSbJxZQTYPKjBDWSeKYUauL3enTX90iXy2Rq1YwS3lUQ6M0tXhJKmriYd3ET3uP405f6BAoMmOlR2MHxjaLVEd571a9tjjH9BUnWP3qPfs+R9Y0Hv99377v54Hj7tJ7iLTwUJjhRDciCkK2Tp87cqUVGhoOZqmEUcozanVJYoGrBdE/s3p9AAr1Gqqh4w5HNE+JpNdf2wBJwnBsojBAM03y9Qqdc8sZNXwiXnXYTmdyvB/6vb/ik2/6J5zBiJmrTvGMH/8Brnja4/jwH7xpl7bLJOIg5Cvv+Qg3v/xFXP2sJ/GZt77zQIcho5Dn2E3XMW53cccWo3ZXJDd2aFkcgTA5KuIoEj3/dJdntbvY3T7leeGsNP/Iq1A1QeiZyNgON1pEUUSuUnpQutI7z6syP53h3Y1CHpLtbbxn2VjdPrkUSjn5HJGQTE494VGZqXN5uoFROBiOOAlF02gcF4a+e1ETVn8gsPuQLR4TBmQUhkKoKW1XSBK7dmpCAuiAKjxN+LlykcDxMIt5nJGNVsgLs+LJ32WO775ogcRd6sfmssQX+j7dC2tZC0FRVWpL84e2T3c6yAsEkkw9/XtFVZm+7PiRxKB8vYpWFO0/IfI1g2oYGehCVifgASMjQhXq1Uz3JV+r0Fha2EdGAyG2pedHWTtJVuQjW2A7w7eddDcovv+jsPqTeFiSuKyo1OZns+3HcKtNEkVMXXESb2zhDEbUFqYpNg/2yDwsJsOFXLmE3xaO41EQkK/UM1WwfL0q8KipS/poq4M7FtZbuXJxl7nu973ld/mDp78QP3U0ByGDW5md4qbv+GYCzztyqn3YDRRHEa0zF+icWyZwfaz+AC2f20eAmcTJx91AeaaB0x/RXx0Kq7ITebQUdte5sIZm6GydPs8t//gf2KMxs1dfxuNf/DzxEEgy+UqZqqEwf/VVhK7HaFP01Y2iEKAarm+h7yBE7QyBiV/mz77tZbTOXMi0UFa/fBf/8Mpf5Pm//fOsHbDYTULVNax2lye99AXc9cFPMtxoZ+pyAIqh8/zfew3tM8vkSgVQRGtLVhXhRJM+2A+2lSYrCpIiZxWm73ho+Rz5akXQupNtI4PQ9zPFPT1F/0ywvAdF6AuhqziKMMulAzHfO63Ixt3+LrEzVddTfReBQpjY0QXpAM0sFsmVHhjX4CDkVeC6DDe2PSR9x2W42aY6P7PLwCJXLVGeblJo1hlvtYWVYSo7cFSbSVTWGs7IInRdwTZt1EiSJGsxOkNB3IuDSEARtzqZAYKV+nnq6fXzxhbuaLxLsG7c6uA7rtDb11QGa1tEcYwiy5jlIuN2l+r8bHZMR7VxZFnexQuQJIlxt894q42Wz+HbLp3UEzOX6q97Y0sUQzuq+4M+Y0I0nDCBVUO/pFneuN3NrNziOKEyd7g0yc54WJJ4dWFmF/EidL0ssebKJVENPIgHNgrC7YuVJMJ1OnW0sQcjhlsdSiMLRVUpz03RX90UuhW2gzMakyDE/ydx/DHX85ovvYf3vf5P+fK7/wsjZ/LYFz2HJ7/shSiqitXtU90xNRasUDWdLCsErkehWcV3XBR1Gw437vQYrG2KyrqY4A5HPPkHXsDH/uzv9vmFyobG/9fem4fJlpdlgu/Z99gjI7ebd6udrSwaUEYUFBVwbGmlLESkS2x7cHB7sGmmtZvesB7RZrSnFQqxEYahEUrQtlunBm0fe5ouGrUslCqKWu6eW+zL2ff54zvnZERmZN7Mey/cqnny/asqb2bEiRPnfOf3+753ufO7vw2B7UGrV1ESBHCSCH2BioqbJWw/8jufwed/9bcQBSGQJOAVGX/2b38HP/7Jf4soiBFHAew0QJz1Wam1QA+g/IEWRzGQyZ9z+TnDMJi0e3js9/4Yg0sbe61bvQB/8E9+BfXTq/uGT6RRjKW7bkFlZQnv+NyD+MK//wy+9InPIfIDLN11K77/l96NxulVuKMJxOzmaJ5ZQ+/8ZVKMshzKS81rLuIMw6C82MTG334Nw/UthH6A1RffQaEMcTLTC42yz7fTz1TgmdbcIh6HIdH0suHwaH0b5ZXWgbtGUZaQxnGx+ws9j1b6HIfq6iKJx3yfVso3kLIWeD7Zr0oCWEmiAG3Xyzx7xsVu0ckCwPVaBYIkFtmvvCDM1RhwggClYsAdTUgNalqQVxZolZokaD99HsPNNryRBXs4QmV5AbKuIY4ilKY0H0kczzp5clzBYkrTFKONbcRhRLtM08Zku0tGYI06DeLbPfDy4dWZuW5imtfvjsbUUu0OEDhuoY+orS6hvrYC33aQpin1268yN2FZ9khtuSgIYPaGEDW1mClN2j3Ihn7VXeBNKeK7L3JRVWD1R5mfdYokiiBIUiEZDmwHgiTtKxkuXkeRYXUHSJIEkqZitNmBbGjwxhOY7R5at52GqCpkQbuxDSTEjxVkCVqtQlPsJJl5gLRuPY37P/YBPPbFL2Gl2Zra/oUz0+MgSwAqLTbhWw59BkWCOzLhDMYAGJQWG1DLJSoUTK6UTMGJAs6+8qUwFhr4k1/9cJHQvXDbabz+F3+Kkt7HJnzLRm1tFYIiFSs+hmWx8ZWvUQGfegBErofIBT52/z/CD/7vv4jq4gJGnTY6T5/H4p23YHhlG4MrW1DLBrQGJZkkSYzRxW3ybEkSKGUDpcUFBK6HL//B5+d6lQBAygBnX/ky9M5fmfs7i3eehVwuweoN8NjvP4xHPvoZCgoOI3SevoDP/PQ/x9s+9gGo1Z1Vh6gqqK4uobq2AkEUrlt1FzgulLIBydAw3mpT8k7JQOT50OqV4vdYlp0ZJibTC4Nd8B236LMD9F04g/HBRVxVoNbJw4YB9azz+YQgy2icXrvh6Tc5l3q02YYznEBvVCEopGCNPH+mMPOiWFxHkqaCFwWi1oUhAAZqlVKWplFaaNBr+QFKi00SxCUJepc3YQ9GECUJiRahd3ECXpHJ2zuOEUdhweOXDB2TzQ6xMlJy8szbMbkGIW8tSJqa6THoIcywDOIgOLRQKd9tmZsddGUDerMGvVYBw3Kw+kOEjgtJUxFncXi5IZ16lcHk9fi62IMR+pfWIakK5JJO6UL0olf92+eEYlOtVRCHYZbYAugLDXACj83Hn4LZGUCt0XBmP8lwDlFVYCw2Cs/g5btuBVgGkeuhvLJTgPOeMcNyxYoojiKAZWdWA9PgVRlpxqcGw2Q3P63Cc2lzll6IykoLkqaic+5SsZWKogjdZy6isroIhkE2OfcgqjLkUglIE7zk+1+Lb37b30Ng00XEyyLMdg9Wf4Tycguh65ORfGuHj6xWSvgfn/jcnhV8jsBxIQoCjMUmhMkIaq1MKrZWA/ZgSBJ+18OJe14IZ0DRYfl5csYmlHKJeOIH+HQkcYLWnWdw26u/GU//+f8oCrmoqZB0FW/9yPth94bYePxr+JN/89szbaPQ9dC/vIE//Ke/ip/49G/AGZtF6kqp1YB8g4aM7sSCXDYoeV4UMMyGf7UTSzMzC0GRoZYN8uQBtSam/TymMX3DFsEG3MGrpiQLHlFKOnhZ2sPeoCzQG6v8nWx1aN5xchV2b4jhlS00zq6htFCHZzuIR5OCxx2HIXhp52Fqdvu0+syUynZGxZvGPJZOlInjcpZWHEVQqxUIPEeiuYUGOEFA59wlIEnAiSL0ZhW+5YJlacEThxGSmNKMpl0t0zTNqH4lRK5HQ/FaBUrlcLqMyXYXAANBVSDpKqxuH5Iqo7RQR/fcJXA8B992IKsqBE1BFAQH+irZozGsLg0dtWr5yPFugePSeS3pYJIU9mCMKAhpJ8ayxXBzPzwnijjLsuRytxAXA77+hSsYb/cglzV4plMozXLJ8H7QKuWij5SfyND30b+wvrOFdT0oZQOCqsBs9wGGlGzVE8v7nnxeFFE9sUwy2DQtts1REMDqjwob2iSOMd7uUq8vSWjLnqaw2j1YgxF4RQLLctAaVZi9EQUjN2tYvuOWPXRE8guhHvx4u0vbSYFDdWVxR44uirCywIt5YBgGk24ftzZqSL8a4/O/8lt45v/9CwiSiLt/4HvwrT/+ZnCSAFGRYXbIq2b6e0niGIqh4cy33IP2MxfnDiWTKELj5Cre+tvvx/pjT+AvPvUf4Y0tvOD1r8Yr3vpGTDp98KKA//wvf31u3z+NE1z6q79FkKXnxEEIThKvOxJsGhzPE91PZMkPJtsi7z7nOb0tz0RM4hi+aRcrwekFRD6k7DxzAaEfguU5nLznBTOvlw+d0yQFL0vwJybs7gBBYwHuaELe8fv0268V7sSEOzbBcizUWgVhEFAcYKVUOExWVxbBCQLtDF2vkHUrJb0IMABAHt1TgRz5NXE15DsJTuAQZrROTqTcyPJiE1EQwnccGI0aOJ5mAJ7poHFqNTNk26LhapJALulQaxXYWb84TVIs3H4agUXiH6QppAM43rsRej7E3MogE/YlMe3eF245CWswgqyp4BUZge0c2M/2bQfmdhdidv9bvQEYnptLJywUolmrMj9HgetRfVlehNUfEoefY1FqNTDabGc0w+cYxXA/5B8q9DxEYQhBFsFxPHhdgGc54A8o3tPYXYgFSUJ5pVX4BZMooAGO5zP7yYRUX3O+rDiKEIcR4iCEqMioZc5/OYh3vfOeOfcdGX0sX5W6ppn5OhhAxos/+83fRH+zz+pf0lVYvSExeVZa8E2L2gu7zsPCbWdw7pFH5yoh0yRBZXkRl7/8BD779n+CyPNyMR3+24f/A/769/4YP/kHHwHDsqTuHE4KaX2e9xn5Ab71J34YX/79z1Oa/NQWj5ckvPS+74XerCH2fLzoe78DL3j9qwuhiGfZ1JJQFXSevbjvd8YJAvoX1tE8vbbv71wPyosNDK5sIQoCIKPYcVnPd7d7HcNQEpMznmC82QEnChl9zprhSjMsC4anQF6tLoLlmGxFZRTDQ+qZE43UM6ldFwUeuIxK6wwpa/Mo7ZODtu35MYuqgjAMsP3VZ2GPJ0iCkBLvq5SdOe0tU15qQW9ks4BdbStRVymxSteID52kRfjIQaC50wKiICT/oyTB4m2nIWpEKS5CgLN7jlTBFpI4xmS7W8yXAHoo1ddWIJ1cRRJFRe5pHEWZdwlDMY2HXP2Kmlrcl0kcAymK96qsLCJNgdj3EToe9Hr1wIdD6Hpgs3keAPAyFX5ZU4FM3QzQDqd3cR2+bSNNUihlA41TJ5AmCXm/9IcoLy6gvNiEbztQygYiP4BvWpDzXY61T2j8oT71NxqZ45dWrxbTWt/xUDc08Ndo1J57VOc2rDkOsqqkXuIWhSavt2GvjfZYmnKCAIZjEXo0jAozcQHHcagstzDapP5rmqTEfMlENkhTxGFYMBuUcmmuB0r1xBJRzFKgfnJ1z7Z1/W+fxF99+j/Nl7IzQOP0KpZfcCs+/mP/qMgpzREHIczOAI989DM4/fK7C+9qL1PK1U4sgc8EQkq5hJ/+44/ic//br5BohyUPkVe+/T689E2vh6irSLOnw7RQJLQcOKMxOFFA49QJ9M/PT3uKghD1jKlwPXBNC+5wjDRJodbKxfkiHvxqEf/GsAy6z16E1R8g9EMsnFnb06ozuwOIukpyec+HOx6TEjC7qZM4RhInMw6YORWSFallxwk83HHGKCqxcEcThK5PfdfMZfGwCD0P480OoiiCpKkUBMxxRL3MCoY7MiFqSja0jTDp9FBabiENI9j9IeIwxOpL7tqzYNnXwqBeRRxGBTOjsrywr/BmNxRDx+IdZ9E4s0YtREGYKWruaLLTzsxYOQzLIk4SiFOc7bxPLu3aNe3WaRwWpVaDhGS2i9DzUV5eKOoAqX2XC7Xv1axsWYEvaLoA7fo9M4SfJWvlbB+zN0D/yhYC00bguMScSxJwDM1g0ijC1tfOQVIlRFECaTDKfh6Dl2XKnN0Hz7kiHnoezC5ZUKZpAsnQEDo+lu48e93T+qNyfUebbfAieRfwmgyzM8iGPVMtB45D7cQyJttdBK4HWVdhLDSyVUuCysoiOQ1e0bOJeIjI8yCXdLSfvgA72z7xPI+Vu+/cQymadrGbh4/f//N7lJ45RE3B333fz2O4voWLf/mVub+TxjEe/cwf4UcefIBWZa3mHqdIQZZRXmqCYRn8yIO/RP5WUYjxFiV4C4oMQRRptjFHKBL6AdIkwbfc/yace+TRvf17lsHaPS9E88z1rcI9y8ZofRtREMDujxB+NUDrtlNonD5RmGXlxar9zAX0Lq3D7lF7rPfsJdzyqpehdduZgq2DrAfsZX16ZzzBZKsD6ezJgpuct9DykIg0y4cFADAMUgBxFIPlObBJAklTEHW24TsOksx06zCr8DiKMLiyRQ8DmailuXOmbzlI4xhGq5E5NsYAT+/LAJBkCVKjBn2hjiQMj5TNyXLUvsvZI0e9//YrtJwgoLTYzPrTO2k/DMNALRs0EFUVaoGx7Iya9HrB8TxqJ5ZRHg/QPHtyT11gWfbQ6mDZ0OFNdnxTQs8vglEAYvsIsgR3bMIbjMBwHNRqCc5ghEt/8Tc4ec8LoZRLkDS18CHS6hU4gzEizytUu7WT+4u8nlNFPCf8c4IAo1WH3RtCrZVRvqN5w0NhrwaS0kYzHtlgMDeXkRfFGSVd6Pu0gs8oe6XFJqpry7AHI/LRWGwWidpqtQSW4+COTfTOXcLaPS869DEON7ax9eQ+Sk+Qq57eqGe7GQbpPrMR33HRfvZiwUgxmvU9F7ZaJm+Y6Z3M4u1nYQ1GSKIIkq5BLZfoZp8SiqRpmjkPrqJ5Zg2Dy5v443/9fyCOYqRxDEFVUGo18L/83ocO/bn3g2fSg98dTWjmIfmwekPys55iVCRJAmc0hj+xidIq8HCGJkbrW5SYlMm0lZKOzrMXoFUrxHuulgsbWj6Ty5cWmxhvdrL+bQqtWaW2QESCjTSzlaC5iYrK2jKkbgdqpQRlan5zEGh1ZyP0g+KBLqoKuheuQMwKOiknu2jddRbOeJKxjRKwAg8+G1omQVj891Fx2AeNPRghCkJImrIn1Hg3lJKRFepoxks8D0XwJhZYgUd5eeHIK+55Ipx5n+l6E3xyj58wW5jYgyHiYGduxAsCIj+AIMtwJhZKC0SJBMeDRUK03uw4Q9eFN7FgbnehVKmw63URnuXAm1hz3x94rhXxbOjFSyLiTKoau/43vIADKKS/getBVGQKbGaYQ/UDRxvbYDkevKIQ33O7i8bpEzOFZLTZRpomOz3BzHrgKPBNi5gvmM9MCb0A7sREkCX/eJmgYzfWvumFECSxeJhwHDfjFFcgE27kvOg8lGPS7mHS7sEZjFFealJUXXdAdrJhCG0qI/UNv/hTePlbvh9/8an/CGc4xi3f+jK86Hu/44bZC+eWB/kDRFAokGK6j5wfe+gFVETiGGBzbvLOk06rV7OhI5OJg0p7KJSKoUM4LdLql2VgtnsU9cdxiMMIar0CUVdpPmA74EY8qmdOoHFq/q4j34k6ozEYhkWKFGmcgmGA0UYHnMBDMXSS5Tsu4iCCbJCAx/QG2Hz8KVSWWyRokkQs3n4Gvu0hYmiFWFq4sUPUHGQ5sY04DMEJAibtHpIovurQdt5KnSLKavOvwUPAnZiYtHtIM2O63VFn0wg9j3bCGY2Vwi6o7z7vmszFSwzLFg+AfIYCAKGqwLT6xc4hjiKkKSBpMmRDI+sMSYRS0iCXdCQRqYmTOCaWjGGAYdKMp+5i6c6zkEsGqqtL6HxtfjD1c6KIO+MJhle26GZDCo7nMNwksyKWZaFUyzckDOKoKC8tZAMpC2mUZH7b+5+ywPUw3NhC99xlGI0qtGoZrChSWnomImGyp79kkCdI++kLSJEijiJUlhcx6fSg1SqHKmqNM2tk27kPmmfX4A4mMBbqePX/+jZ8/t/81h6xDiPy+I6f/bFiFSQqMoW57rqBPMumQII0BcOxlBEpy4WplKxriIIQw/VtNE6fgKDI5NkiCHvaQY3TJ/CGX5ifv3o9UCslmO0efMtCmqTgBA4cy8EejDLqnIjyEvU/m2fX0L9wGeN2F6KiUMpOrTJjmcrxPOonV2H3h+AyOb6ka2B5HvZwVOgByApVACcKM3xmToxh9YfgM1dFVuAR2C4Gne25x+/ZDi7+5d/AHU1o9wQGQRDCqJax/OLbUV5ZQP/iOsnLOQ6V1UWMrmyBZYn+FwVUDLRaFTpDvkGcKKKx0CBvkakB3I1AkiRFBBuyqLmcM88JPOzheG7U2NcTgUtzA1Gn3ZQzHNOOdI7s3R2O0WM2wGb3kFTS4U+soiVWWW7NXLtxGGK40SYBFMOgvLSwZ46llEs0PzFt0kKkKZzBqAjsnmx3IWcFvLqyCLlcgjMcIXEjNG45hTiI4AxHcMaD/LSSluWAhex1FfE/+ZM/wcMPP4wPfIBSw7/4xS/i13/918HzPOr1Ot7//vdDUQ42fnHGE1z44mNF5qDZ6WG03YViGOBFEZWVFrzRhLZeN3hFTk57cVFYdyPvnSVJgmGywxWdFiHluYVIUwyvbIKXJfCyhM0nnoFS0qDVaxAUGcP1TbAMW1wcHMdBrZThYgJ7bCKwbShlA97EQuB4qK3tz4fPwYsiXv8L78QfTYUO7/wjh1f/9N9HaakBQVbwyrf/ECaug0c/8mlqCaWAVq/gdb/4TrRuP1P8WW5wNY04ijDebBdBylEQYrTZRnVtBd7EKiLUeFGAHwSIw/CqvfyjIt8BHAReFLF4x1kIqgxnZFK48YicKSVdQ+j5GK1vo3ZqFYph4K7v+Xa0n72IwHag12qon1zeM+DTszSZ0PXASTQodQYjMqViWPQvrYMXhSLPdeY7y9SvHMuCz4Zykq4iOL/DuU+ydgvDMBhe2gASmkGImorxZhsMGLCLDfQuXMHKXbeCWV5AdWURoqqQJ3a7D3di0cJAVcBMWbXmkvmj5EymaeYjn+ysTuchDqPCeydJUoiqPMNa+noFGl8NoefR/CH7HgRZRmDZwK4iHgUBvKEJ+eQpamX4Pja+8hQWbz9DTpNRhPFWB40za8VrjTbbSOMYciZWGm+2IZw+MXOOcrq03oyQxDH6F9chZeEqoqZCNjRUVhfBi2KhiGY5lhaLYzJhMxpV0oUEAaqri0Ubcz9ccxF/3/vehy984Qu48847i5/9i3/xL/DJT34SjUYDH/jAB/DQQw/hbW9724GvM7i8SYb/GVE/TRN4IxPlJeqD86IALxMO3EiEnofu+csYb2e+LWfXUD+5Orf3t7uY2v0hrN4AvCwjdE34Ng0jUmQ9uCSBbOhwTQuMICDu9rHykrsgZbSo0WYbcklHZXUR1dUWuueugFdWwSAtZN6RP19gkCenk6OhgO981z9AFIT4f97/ITAshzgKIesa7vt3/xK3ffsrMN7qQKnQQ+bMa16Bb/l7r0PkU4J78+xJeKZFxde0kDIMOJ7bswVOoigTstC54UUBztDB4NI6BhvbELpCEY5L7pGHW+0FjpulgMdQq+WZHmro+5RkFEWkwo1icJII3z44HJgTBCycPYU4DBGFIQZXdgJ/BVmaYY9Iqoq1F9914OvlQpZpVlAeeWZ2+qQVCEOwmR2xbznwM25x6PswGtWZfub0ENRsd4l2CApzjqIInMAhiYDAdqmNF8ZIwwisqsAZTaDVq4UnOMfzOPFNd2G0vp3NPFIqqtkDL/Q8yEYNo81tRH4IUVMONPNK05R8VIZjEr6BtBPzrkO3PwK0UrHydkYT8CIPz7TA8jySMKRB6ze4kOctkRxxGM4NEU+TBClL3y+RDShNKL92c+56GseUjJWmZLmcfd6cZUasmr0POo7ns8K7k62Zt2MlVSn+Jg5DbD91nhZvrovtrz6Lxhmy0m2eWSve7yBu/jUX8XvuuQevfe1r8elPf7r42Sc+8Qk0GtT3jaII0gET3jiKaNW9sYXAciAZKjiOBwMGeqsOjmPJAtO0YLZ74HgeerN2QwQgSZKgf3kTZm9AqxeGQff8FbA8j/pVrD735BaKQjHIYtI0G2gJUGsVUnrJMrqbHTLWOUEuazk7g4JmVYiqTCsXjkPoUorL7ocW5WOGcEYTeBMToqIgsF14loPX/+JP4Tt/7u144uE/h6DIWHrRHYgcsgFQaxV0nr1I3uSdPk6dPg2WYyHKMkLXK2iMSRgSL1wU9jzIWJ6i0jzTgqDISOMEznCM0mITrbMnMVzfwlc//9/w5c89jM2vPgOjWcN3/MyP4ZVv/6F920Kh72NwZYtED4IAMxMsadVKER2WMgw2v/I1uGMT9VMnwPEcJv0ewhd5V6W5EfWTK8Qpc9kj1wqG2fX9ZPmZWYuDKHMhtHqFRGAcR9v67H3VRpUSeCY2OSsmCaw2qQadwRgMR/16dzzB0p1nqTceJxBVFZUpoReQDZx16rV6pgXfcuCOJxAUBXqzBndsFt+rO5ogjeN9U2JCz4c7HBe85DiMinnObuSCrOJ88xz0rPcchxEEWbqhOzHyYKf26kGccElToZR0WpSABovzeuucIIBlWIpzm1jwHRfeaILAssmOwQ/A83zhOc4wTDGk5CUxU1AyM8Eau8HyPERVLjJI87/NXzNNU3TPX8aFLz0GpWRQjJwmI44S1E+t7vLDD/d7GzDpVYiqDz30ED7+8Y/P/OyBBx7Ai1/8YnzpS1/C7/7u7+LXfu3XZv7985//PB588EF86lOf2lPIH330USiKAnOzQwomx0P3a+fAiQKUCvkCL734doReAG84htMfQW1SSyLyfOitxtwn61EQhxEG5y/Dt5yi1xS5HuSyjtqZtbk3ued5kGUqtqPLlOCe/15gOygttxDYDpz+CGa7i9DxoJRJUGJtdSFXSjCWGllae4DSSgt2b4jQ9WmIMZqAE3hEfgReEWG0Gigtt8DyHLkObncLvrpcKxd+J4HtorRE8wIzC5V2xyaYNLvRNAnhxIZULoHTZXApA6mkFZmQckk/8EIEAG9sYrJOvhtAAmNxAUkS0QofwIU//xL+7H0fBKb4sozIY/WeF+KNH3nf3N2NO7HgDcYQVDr/xLVNUVpuwer0s+SdCJ2vngMv8BDLOjhRhOe4OHH3nTsBDjx/IOPCtxzY3T4ABkwKKI0KBEWG3R8g9gJwsgitXjvUwBqgGy+wHdidAQ2jtnoQNBlKvQKWYWAsLRQKx+m/iTw/69PzCJMYQX9MA+Ls3IeOB7liIHR9OL0BQtcnK1xDBydLkHQFehbC4QzHSGOi30olnbIfN9pF6k0chDCWmmBYFpPNdqFOpPdxUVlbnnuNh44Hq92DkHmW5Mdd3UVvS9MUw802mDCCkInlgokNVuQRWA7trBpVaFkr6lqQi6vyLFXfIp/yJOOLa616MbwObAeh49H1XNaJax6EiFwPDEdD7HnXSPfSBgZPPANOFqHXqxA0Fc5gBL1ZB8tz0Fr1PcEi9nYfSRIDaQqlXi1aift+jjiGMxhTiLRErdf8O/ctB+3Hn4bVHUCtlhE6LjhJgFIpY+kld9Dq3/GyujKEfnIZL33pS/e8x1Wv3HvvvRf33nvvVU96jo997GN4+OGH8du//dv7rsRvu+UW9Hm5EDs4t92K/qUN1NeWUT+5DDmz3rSHI1i9YfFEz9PpayeW933/vBeXxDRUivwAkq7ObNWTJME2L2Gy3YdS1pHGCQLXhbG0gMVbT899yj/55JNF68heWsZ4sw3PchB4PrTVVSy/4HZwHIfQ8+BOLKx/5WtgGQ68LEK9+0UYbbShlg2o1TK1HjSVeOOen63ud7zUJV1D6HoFNa534QqWaxTI3CtfQRwGqK4uF62X2toKGJbBNiuSE+DSMuIoxNaT56CrJZROn0YcRNjq93DrC+6EsVA/dP5nFAToXVjHqRNr2U0dAAzRBmn7yeD/+jf/cKaAA0AaRNh87KsY/cUTeOWP/VBxTgPXw2izDU+I4aoxmmfWCsdHThRQW13CqEzb/8B1ofoJ4iCC3qxC1FSc+9pTONFaQmA7hQTbaNX3iLB2f4YkisFwLHhRRP/SBlKtBEGRC1P/2qnVA2cQcRRhvN2lG61ZgnLrrZlgi1pNHMdRoPec/nMSx5nIiAJ5n3zySZx54Qp8yyl8YnzLxsKtp2cHabsUiVEQYHBpA1y1UQSelBabtDPSK0XbI/ID8LIIrVZBX9GLdlKSJAhdDwu3nJp7jeeqQpbngTSBb9rQF+qFU+e02GgC4Ja77kTsB0jSFFGdrmNvYmZiPRaGoqNx+sS+PfkkSahIZ2yh6Z8PLm8ilsktsH/xCpqtxUJU5ZmknJU0FdZgBLPTg9CQili72toynNEEVqdftJn0hTr0WqXYSY82tnDxyjZuecFd5FnCkFozcD0snD25fxD1i5KdkO7rZFSZ3T7KDI/++St0HcQxkjDEqZe9BM0zJzHZ7sBhTXgJB0eNMd9+7gazUz70oQ/hiSeewMc+9jHIB2x3SYG1U3CVsoHWLSeJeD8tf96V+5cmCRhhr09ynPUBneEEoedlW37KT2R5HmanjygIC4YLy7JonF4j34NuHwzDorzURG3XVnU38l6jWinTReYHUEsGeEGE1emhvNSCIMsQZBkn7r4L4/VtyJUSGDCorFD/W1LV4jPmNEaA5Lt6o7qTj5gNpQBQhmO2uhMUGdtPnydBTclAbW25yJ8UNYWyMVkOoeui1GqA5zlEPjnC+ZcspHFypEFXEifkLZMN3/LwYmO1jvFGG0/92Rf3VR3GfoAvfuwhvOzNf7cYxOXDX32hDs+00D13CZXlFqkBmy1M2l38xX/4Q3TPX8bSXbegfnKVHCKjBPZgDKVeIYOwPCsySWD1hlDKpX2LMC+KQPaR4zAsAiLy80ntMOqT74fRxjbiKIaUJQ3Z/SEap05clT8dBQEGVzYLVV8eDqA3arCHFAKepikkQ0OQuefl2E2/Cz2f7Byya0FUFXhjC5KhzbR3cm66IElQKqWilZMkKSrLC/te45wgoLq6hM2vPAl7aELUVYgZnREAhuvb4AQBsiwVwdi1W06RK+ClDThjE4Iig+N4+LZDK/TMF2Q36FrYQhyGSMFAr1eKtkfkB4j8HZYPL0twJzZKrbS4DvPPa/eHM9m21FKyM7qeVqzWrW4fSkmHO5rA7A3gjkywIk8q3LKBKIwKC4SDivNRhEBXAy+JEGUZ5ZVFeBMTie2iurKE6soioiCAO6ZsBKQpIs/7+hfxXq+H3/zN38Rdd92Fn/iJnwAAvP71r8db3vKWPb+b97fzJ2WSpCgtNvbcEDM5cxnfRp9ylLOHI0zafSBN0L+4CYZnIMoyPNsGkhQn7iYzIl4U4I1NqJUSCSOSFJKu4uQ9L8oGZcTz3G8Sn2SWlB32YjEImWy2IepakWriTiwYCzufQa9VwbIseY2wHBon95otTSOP8UqkpCD+q1VaASklHc5wDC6zpC21GqgsNpHEyQyzprLUgm85mWtcjOGVjYLiZWdmS6WlJvXn5niGzAMn8MVnZnniYefRWrVTq5B09cAesz0co/3MBYiaCkGWihgtAGicPQm7N0RpaQGiLOF//J+fw+/+1HsBUMESNAVquYT7P/FrqCwvQKtVcHFzg/IwpzjfyOxMcYhedy65z+XeBTtk19/uJC+FBf88H+LlSUN5lN1BmLR7YBgWsqHQKrA/JP98ngfLc2icXgUniuAEAfZgDLmkz7VUzT3gpyXeSRRTG9LQ4Ayn7hOgsLctLTQK/+6DsiqL18zCLSqrtNstHPYMvYhVA6iw5px5Ch7JEoDiFAl2jnG/8zNu9xBmwSAMQ+dJ1FTyj9/1jBFVBe7IpD50Zi9RtGmYHXfDaeTFfua/s1W4bOhkTyDL0Oo1+K6LOCDXzPLSfMfKacRhWLh6Tiu4DyMwmoZs6NDrFYBhIUgC+FUJC7eegiDLFNCevYyka1lox/zF0nUV8Ve84hV4xSteAQBoNBp4/PHHD/23ehZ1lPNs511cec6cl/l8T58wIscPIOlqFtbrIxg4aN12GgzLYuvJZ7F4+1laladpJlveBLKYKbs/RG1tGUrp6m0FZzhGaLuQDR2h56P75DnwMvGf4zDCuNODUa/OUOAYhoFWrRy4zZ+GqCooLTVhZpaWRqtZMCLyFcqk0wfDMFi+42xhBuZNJXmLqoLKcgvOcAKWAdRKGZwokuGRomCxJEOQJfQvXikm8aXF5oHDYo7nodWrWP+bJxAHIeSSgdWX3FkMC2/51pdRiMQcsAKP1btfAEkjniu1t/wdx8eI4s9kXcP6l5/A7/70Py+UbwAQ2i5ML8Bn3/U+/NMv/zEAgO+2wYliMSwKXY+8ag65tWU5jrwzNjvZTcLAmLL2zWF2+8QPFwRy3LPsgmJJtrOHs3GIgnDGbIp6+UlWkMhHewfpXA8c17QoLi6K4QyGSOKYVuPZ7oUTBJRaTbjjCTG9yqXiPmEY5kgDxjicDRDmBAGR54OtlADsFExqT1HLgc0WZaHrYrTVAwO6ZiVNmVm4BI6LwPPB8RycEc278l15EkWorFLaFy/RUNSz7KxtwaGy0spscFVUszxQGlIz2P7qM+AlCWq1BKVsQNRU8NLUNZL5GrE8X6TIq7UyoqcDpGkCo96AZOhoZC21NE3hjMZwxxZYnoPRqBb1KQ5D9C9vUNIXw8DqDVE9sQRvYhUpUHqjeqj7Pjcfy4NoOEEoHgCcKILPkrs4QYBcNgB3Mvd1bqrYZx51Kc9DBLNj1zivf5tvp1iWRcpxCB0PvCRkogYeoq7CHk2gAkjCELwiIQ3jYjUVej4l80zFOQHUh8wHZoVVZDZwAJB5GUvgJAGeZYHleHimWXhuXA+mY7ymwbIsSgsNKGUDPZ4HJ+XezxH4KV4wwzAoLTSgZgNiNmPCpEkCXhIxOhdkEnGyY43CENtPPgtjoQ65ZECt7G1JJHEMuz9E88wpMBwLb2zi8l99BUarAUGWUFlu4e+8+fvw6EN/tCdjk5dE/E8/fh9ElYqIUjLgTSh4lmFJ9SnpKtrPXMAfve835gZKJHGMzjMXsP43X8XqS+4qPDZ65y+hfe4SRElCo6QfiZeslAxiGGSeJtMr3zyUxBlOCktWIQs88LOikqaAsVA7MKgi76FP2l0kYYzayRWwHFt0B63eAL7twHc8lFuNbN7D7TE6ioIAk60OBEWGxHFgeS5jmCxkTn9C9sAZkfDL8SDr187gEiQKGt6dPMSLIoyFGszOAGDIEnray0irVijIY20FcRRDlCWImlpcT+Q70wXLc0jiGIMr2xBEAVpVR+B66J3fhGRoaJxahVYnnrRnWoiDEKNtF5OtDtIkhadIRTSaPRghCSNUT66QSZnvw1g4RUKolUVYvQENjA2t2JEaC3WMNtrE0NJVlJdaqCwvQM3acaHnwewN4QxGUColJGGEweXNgi3imna2k8/qiOuhf3EdAArPdbPdgyBJex5gVn8IMAwtYKf+bV67ic2uc7PbR+T5KLUa2Lr4HCziu5H3D9NspSIb+r6mVyzP09PW9cBLIqQy9bvy4NTWLacyhSUHQZbI97u3Y+WY98qm4ZoWJlsd+vcsJ49WBjTEA6i9kCQJSvUaBEmEOzLRPHuSVuJfZwiSBKNRhdkbgmFy06ClPb83fVFwU6uwNBsk5QNluzuAMxxD1DU4wzGsngKtTm0gUaHefhyGlD4k8FTQh2Pq0Wu0AxputPGjH/llSLqGR/79p8FJIuIgROv203jLgw/MpKEkUUQr/7KBNKHd0XijDVFX0T13ad/PzfIcuucuY/UlxOmmsIAUrbMnKU1nNAEviQeufqbtAvJziamuRZqmMLt9uGMTSRgicP2ib57vcqonFsGAAcOxB6bIFHFiUYzaiWUMr2yh8+wFVNdWUGrVcfHiRXiVGpSSgdHGNkZxDL1RQ/XE0p72Q94+SeKYQn2ztkC+mwk9D3Z/VPR/4zDCJBOpXAtEVUFpsQGzO9iTPKRVKzSQjxP0fWfPIkyQpLnnhc4tOUIWlq3ZIskZmxhvdaA1KpA0pcja1Bs1qOUSWetutKHVy+A4Hs7YxObjz+DUy18Cb2JR3ibHQTH0TANA54vj+Rl3yRyKoYM/RRzwytoyVl5wW1FfKONyiMHmNjiWg6SpEBS5mKPwoggkyWyMHMvCt5ziIcEwDFhBKLJAARrm9y9vFmrgweVN1E6uHCheTNMUVn8Ib2LNzADm4aYW8SSOYfYGhW1pEkVgsBOW6k5M+Pb88FmGoWHhpN1D4HpYvvMW+LYFZCnwerOO0sKOZwIn8LC6A2IjsCxCP4Ax5SMRR1Gx4sl9L0ab22ieOQk98/7wTQtJCizecbboT5aXF4qVwTcCeqNGg6w4ASfuWHtGQVCISiRDm3szMRkLgHY6gGvaEGQZ/thCGHhY/8pTMJp11E8ug2U5kncLPG13k4SKZxgVCeq5BzTDsvjh3/hXeOMD70b7qfPQGzU0Tp9AkiQYbWwXDm+8IECrlosVrDMag+HIg6J+6gQ2/ma+N0QSx6id2kkWDz0PnLCzUxIVBb5p7ynioe/Dd1xKespuvlKrMXe3Yw9GcIbjLDpMxKRN/heyodOQLkkQOh4kXb1qDFgSxzPCkPqp1cwTe7kQ4+Q3eP30CaRxUgQGTyPNLA4C10XviWcQuC6iMEZ5qYHWbWeKHMrpGQEn8PB9/7oUk1q1ArVSpt3crp1ZvkC4Gi11z2fJZg9JksAdjuE7pHY2mjWkURW8LFNB5qlQ5y1EUsEy4Dga9IeuB6s3xMLtpHdIori4Dg6rAcjJB6KmzIjLzN4QsqFB0XXEUYBxu4fGqVW67rLfkwwN9mBULBZDP4DeqGXKWLqu8xZxDi/Lwc3/PY1Jc1GwiQISGrEcW7RtnPGEIgTzRKX+fC9x4CYX8ZyyxSsyfMvGaLOD+ulVchAMw8JsZj9wPI/qyk47JE/OsAcjhI6HSbsLPUsO4UURtZMrxLFNEhgL9ZleYf4+O4kk9LTOWyvGUhO1U6uY9hg+6o2SZh7ipNzaac/kBeawr7W7iHiWRbS5lC76NIqx9IJb5xarykoLo/VtBA7ZXBrNKnyL8gpZhoFsaFn0XK1IIzdadZjtPpIoQhSGWcQcM+MBDVCbYvUldyJwPbpINZXsRtukitV2JaZzgkCKOADf9APfg7/9z38K7HaJZBhUVpcg6Rraz1yAMxiDXVmdVeVFO3mMAN0Uf/Xp/4y//uz/DQbA2W99GW79tlfAWGxgst0FL4p7VkGeaRcikjxIm7b+EbFhShqcsQmrP0Jtbb6KsTjk7HzkLYm8GObpQlfzEI/DEOPM2pgXBNjDMZzJBGq5BDEFYj+E2Rug3GoSJ33qu/AtG5zI7/SBr1LUoiDAZLtLg2RVQXmxSUHlDIMkihBm/jfXk/lJ86EyrP4Qvu3C7g9hNGoQdQWT7T5YnkV1pUX5nllbKYeka4ijGKHnw+z04U1syFUd480O9EYV7tgkhkuWdXoQeeAgpAmZjDEMMWVGG234rgVnbELW1eJ1BUlCLXMkTdMUpVYDnChguE6LFSY7Znlq1pH34Yv3mno4epaN8WabRpZJCr1Rhd6oIZxi9TAMA38fu2ngJhbxJEkoAFlVYHUH8EwbZqeHNI4gKAoNKz0famV+n3geche+2A8oCstyEHpB4UPCchxEVcnsUWd7j1w29MhvhsgPwE+t9nLK1jTmFd3cV8U3bXACD61WAS+KSOIYo802QtdDmgJqtQS1UioMpMCyqGT88cMibwH0zl+GnSXpGK0GkijG1hNPY+2eF+4ZGPOiiPrpE0iiCEqtnIXgRkiCAHLFAC8KWeuBrEvTNIVWKRcJSKWlJuzesGBCTCfd5EpLOjaK5kqTtAj+HW60UVnZCcoWVQVKtQx3NEFleQGveefb8Ocf/AT9fRSDV2WoJQM//O/+VXFe3GcmyDMfPdMiyh3PQ8/aWVZvgF955Q9iuLGNMPOTefzh/4oz33IPfuTDD2RMm72WBrzIU4bplEKvvLhAwQ/Rziwl8gPYgyHElb1trBzkn7FAw1PQEDTPRWUUSnwKbAfIrrfKyo6CMk1TjDbbiCPy6Ij8AFZngPrJExBlCSxPhl7TzJDq6hImWx14nkc3fyRgcHkDvCCgura879A3SRKM1reRggqPZzvY+MpTUMoGAtdDmqQQJKH4nq/Hu0hv1MDyHDb+9kkYrQbtyHgeokx04HG7B4blIOsqmmd3WkGKoWPlJXfi8l/+LazeEMZiA8t33gJWoO+rcWoVYcalB8Ogf2mDlMplo8i6jKMIVm+AyM8scmsVuBMT48tb6Mk69GYNYu4LlNUOrVGFxtRQO7FEO4Ss6Ia+T7uszEY3/3ltbZnMwIBip1p8hrJRBJ0D1ClQygbZB293ZwbmZm9IO2lZgjexs1ZuiElnACzMn3XctCKen3R7MMpocQqMFiVgCC55TpRXWnDHFrRq+VBpIkkcI7Cd4oYTVQW+SaqvlOMwvLxZrOAsliWOdb49zIYho81tmJ0uoihG4+QqrZKPsAqxh2OY7S5EVUVgu/BtB/WTq3CGY0QeMTOiMET76QvUny4ZGV0wxmiDHAAPm+ye078EVUE6GCOJE0RukPVHyc+ivLj3vOU7gcriAjiOQ+/cJQjNOtzRGM54ArVaRvup81ArBnoXLqOy3CrOv6jIUDK62e4VmtkbguX5ovc33u4B2Em+YfkIdn9UFHGGoRAKtULCm2/7ySW8/EfeiEd/749gdfq467u/Dbd++ysQeUFxU/CigMBxUVluFUyWaauAT/yD96B/cb3g2APEVz//yF/jr373P+GlP/S9M0UtCgJ4lgOG4yiKL2v9yAZlNrrjCVJk7ZaxCcQxSldpn9nDEaz+iBSXZR1Go1YsAFiOg77YgNaoFg+H6eKYD/an6YxyWUdo25BUmVbySTyjFBQVGY0za3BNC6ON7YLVFDgurN5gbm8YoBV/nHnBAzSkm7S7EDUZ480OBEWCenIFaUIPlurqIoWYRDG8sXmknWjO1qqdWAHD7whlQj8oHspFeMauh05tZRGSKqN/aaPg2ec96jzoIwoC9C+uZ6wOkYaIoBD24fo2tThEAVZ/BHswRBKnYAQeDMdiuL6N+skVVE8sYbzVRWA70KqlGcowQHmawytbYDhSC8u6VtggsCxbZP+maQp3YpKPDMdBr1dRP7kCP1tU5N4puQHf9EKRyXzplXIJgUM72igIIGsy9qbTEm5qES+1Grj82BPZFjOGXqcLu9RqFCsrz7LnBjHs86JIMbuNTTMDGs+09lyw9mA0c4GLipzl7/nQNIrlGsXx3OHhfnBH5KtCKjQenmUj8oNMkShm/uI9hK4H33HB8Tys3gBGs44Q/r6GOvOQ85QVQ0Q/ipDGCVzTIjFNo3LVbXvOZhFVBVannw0vqWdJbmp1xGGE4cY2Gqd33NymE3KmkcbJLrEWgySa+u4YFDzfaQiShOrqEsx2FwzL4lX/8EdQXmxC1rXM8tVF/m5ErxMx7eGcwxmN8cTD/3WmgBfnKgjwyEc/g297x4/sCKx8H4NLG0X8FxgG5RZZI+QOc1TIL8AZTSBpGqI4hmc5CH1/bm8897KWdFpNBaaNuFya+V2W4/YdwjIsCzCzaUG11WVqUU1sMByD1u1n5/59EkUzDyhOEPalf+bHAezwm93RBKKqZmZeItKUkqjEzOu+f3kTHMuSFW9/BHswKu7Tw6K02MTgyhZCeMTvjyLI5VLxXfqWDc+0Zj5fmqaIgxBWb4jO0xfAcjwERcbCbaeKez33FimEUJoKd2JCMrRZi1yDR/fZiyivLCKNIrhjM4vem6C8uDB3NpHDbPcgqHJxjj3TQuh6iOMY7mgChmWg16tkh7vVgaiqCL0AgeOifnJ1T0eB2Fla0ZGIMy0GwzLEaoopJFouGzC3e5gMu3OP66b2xJWSgYVbTmLS6UMtGeRpMRjtTOSTJPP4ONxKmGVZGM0azHa/eFrq9Sp4USRa2y416O5+exLH8CdW8bSHJMLPnoSHBQNmVniS3SBi9tk4nkPk7/BWOVGEN7GgVMtAkh5pYMSLIhVORcbibadx5ctPQJAl6PUKWI6HVt2buD0Psq4VQ7g4DNE9f6VgZvCiAN8KrqpoBChPcLzVgcgomTJUACMTXzeX1k+3DqZBilMVeRJQflMr5RI5A2aGRpy043i5G2anvzO4nQNnNJkJ5nBHE7AcV/jn+LaDOIqhlHceDpwgQDEMsByfPfQomSX05hdx33YgSNLMAy9w3JnhPDni+cT6EWf71jutmHZWXBmUV1rklhiEYJi92/Ucgixn1rBJwVw5KFghF91N2n0wDK1uK6uL5J0eBIhcH3EjRIhsrpckEIpkIZn8wo9YxEVVQeP0KtkJsCzs4RhJuDPf2KP0AQ35nGxhMdpsI/ZNtG4/iyTLqNWztsl03zkJo53e/tTCIUkSGhbbDsztHny9THx0gS/mZ/shThIILLUb4yBEGISwxxP4Y5NawGGE9tMXMNrqQpAESBq1N0PXQ+j5c1ul5cUmzE4Pnu2CF3jIuo7zX/zrjEWmQ6kYYADUTizh8nOtiDvjCZzBmCTHmkpJJYMRKistpHFSKKLKS82rsgGmoVUrEGTymeAFoVh1SRopIulGYBD5AfTmrgs8ox36tpPR6oQde9lDQs94qDEXIomTwtBdUGSEfgBnMERgu6iuLkLS1ax36yF0PJQWG0eSxIuKDGOxAbPTBxgGp17+TRBUGSxDVgaHDbSdOQUcB5ZjEWeruoKad4iWklomfrqXiSQap8l50M7CoPVmbS7TCMjVtz3woli4M+azjOrqEiKfWieD2N/3WKqrS4jmrMJztG47PfP/SZLMCrR22TzkEHUVnCgUbSJ3Yu57DLwgzGSeUttp5zbLqZ0DTgIYBpwoorq6OFM8ZF2DcPoEMRZ4rrgmBHknSWYeREUmwVinjzRNoVbLOwuSfZDzu5MozlTUA4w3tuFNbGpRXNpA7eQyqsstCgXJP0eSgssUr/lDM9+9hL6POAukFlVlz/HyojhznQ+ubGWvSUV4d7ELbAecJMFa3yIrAV2FZ5pgeQ6irkGvVyGqCqR8TgIGDMugvLSU+f2X4A7HxOaJEzRvOYXO0xfIqySOoVZKtFixnQPnb1o2w3LGZjbbSsmArtWg75gBxltdJHEESS0jCkKY7d6BJlksx6G81EIZtDNsP30eSAG9XkPguAhdHy5LavD9cFOKuDsxKX1DU4CEVGpqxYDdH+38f70M4wDv44MgKvJeDqsso7a2TGb+aYrySmtPQcnl5YMrmxAEEVEYkkn8IdsbAN2A9ZO0/WU5jpJgss9QXVkk86lqGZ5F3hJatQLt1ir0XcyNw0KrlAt/hethEORgs9CK4cZ2YZlbXm4d+rW1ShlaZXYHUDrgAsxBGZQ758ozLUSeXxQBXpLgTkzY3QGswWiuMElUFdxz7xvw1w/9MeJdq3FRVfD6X5xNE1LLJQwmW4VmII1jSMbeh4zRrKF/cR32cIQkTihkYp8BtFIpUSZi1lsXZGnGi9yz7Mx6mW7swHFhdnrEp89om/nMYprBZA9GsLr0sDYW9jf8UkoGsVUy29PDIOfMS5oKlufh2zZWX3Q7eFnKmB8pJEOHmAUCMyyL2Aug1isYXtksZhOiqkAq6TC3utRKi8knvtRq7vvgkTQV9bXlIhdUrZb3LGQ4QYA7tpDECTiBRYqdgGJe2BlEV5ZbCF0PSZJAkMTi/JUWGsRvz2LXJI3i8rTRgJKeZKkYgh8ErV6F2esjTRLo9QrUWgX9SxvECBJ4mN0BJr0eygsN8vsXBVj9IfRmHbwswR4SjTWJSX0uqkSrzD9vHEYUHMNlWgZFzvx0lAMDUW5KER9ubENUlGL1EYcU62Us0AQ/TVO4wwm0SumqW/ijQFTkAx0QQ5f4xwu3nCq2e7n501GQ81DngRcEVFaWyHQpjvcou64FNzJyC6CbsXlmrZBg34j8y+uF1RvA7g8RByHsTFswzYzJ8T//85/FZLONc4/8dTF4S9MUr/+Fd+Lu7//umd8VVQW1E0uwhyQw0WoLECRpz8CO43mEQUC9eVlClDGn5rE1uMxFL38A8rI08/0kUVTcpEDmKX3hMowGaRZkQ0N5aVbg5own2HziKeSyd3s4wfILbp1rl5CfJ1YQSLRU9Y4UbcgLAhXs7JpMWBaDyzQolgwNRrNGDxJ7AUkYkRVDdhyeaWPcoSKWP/Rzz6KDdoWcKOwEXcxZyGi1CtyJlc1ceMQhtYxERYY6tdPIRVm7QbRgAf2tDkbrW4iTBFq1TKleLEsGa3EM4SoPPZrDqKif2rF50GtlBJ6H4SUTZn8EJmXBCpRXy6syypqKUovCyu3eAIKqYLLdg2dZqK0ugpck1E6uQJAkcNlQVy7pZIgVkfmcqCrEZtrvOzvwqL9OsPsjsA12hxwf76isgJ0prWfa8MzO3OSXG4Ukjom7nQ23GJYlj5Zsehwfdqh6BDAMM7M6ey7iMOZYNxJ6owpzu0tT+ziGkMXcAdT2MHsDonQJPCRdK2YVu1tt1eVF/PBvvg+9C1dw/pG/Aq/I+Ja//4Oo7kMJFNUdf48oCNB+9iL5pMgSZYVKEnoXLqP3zCXIJR1hmoAXeVjdPmpZgEjO/weoVTDtTrkbgiQVimSWZTHZ7kBQlKlCaIGXR8RgyF5rtN5GFMTQa2UkSQJ7MJph+eTI0+aLwBJFhjsyj7TL40XK4Yx88hW5/NjjCL0AaZSAkwSUFhtonl4jE7VdPissxyIOZk3BUoaZ16EqEAUBBpc3kcQJ0pTaj9XVpZkHH8fzaJ5ZgyCJ9IASBSBJUGo1IRxikZemKYbrWxhv98BLEkSOhW/aNFhNE9iDIWRNw3B9G5Xl1oGLKklXYfUGYDPVMyuIaK4sonvuElq3nSH3wZEJ37ahVA0oho7B5S2Mt9o0X0kAIIVSMqh1yfNk/5E5oOrNGvknpUCSpOQ+atlzLSmK7+yqZ+DrAK1SQhxF8C3yIaCVK5m9iKpCcUmZCEHSNbBZejbLcTe0+IW+j+H6FtI4QZoCSpluiv6lDcRBgMD1Cq7wMb6+0CrEGybDHx5KyShu5MD1MFzfhiiJMDc7iE6e2vd1RFVB7eQKtHoFp15xN5SSfqidRJIk6J6/jMl2FyzHwer0YQ/GWH7Rbdh4/Cl4joMUKRmqJWnR59zN/1cqBkoL+8eSiaoCtVEteqq8LEOtVxBHZEhmD8ZwTRtqSc+sYRezHRGdi/ycJOl+1yQz896k0j+YpTQNluNQPbGEyXYXg8ttJCnQuvU0OJ6YVr7pFD1wSVPhjiZFPm4SRSi16nCzMAez04Nvu2AYoDRl6DYNqzcAk4nMAFq4+Za953dZlnIEtFqlyA097A42iSKELg2S87lGFNAwNfIDVBZb4AS+iE5snN7fYlirVcj2ejTJ8nJJ26GWSxA1FQyjk6tp5jZqZzbJga3DHk4QhRH4jAXGctweIZBer0I2tELoOLqyBSk36TPnqzZvShHnRLo4k8wJTMycBiftXpb5KEKtVcmAP+t5CbIMd2zuW8TzExuHIURNnbvVdE2LXlMUoJQMmO0eWJYDr5AAyB2ZEFTyShBkOTP+EchO9hqRx9DlCjpOEhF5AcVZZXL+5zuSJCFr1jCiecQ1toemWTLFa2eBtKVmHe54jCSO0Dl/CYu3n913CDxvJnLVzxBFMDt9isbLwrOt3hC9c5fBSxKUlAXDMfAmJkLBx8qLKXll3O4idL2CwuYOx5A0dd8BLgDIJR0Lt5xCmqYYXF7H1tcu0D2QxAgsB0t33QJBURC4HsxuH8ZCLbOgcLLIMR56fU7kGM9DKWlwJhbSJEXg0KDuqNeYIEmon1wlR8V0p51Iwpmw6M/Khg5joQF7MAIAGK0GlHIJnWcuYHRlG55tQamUMNnuIQ4i4AT23L9kQrZThvJe+n64lsg3huPACjzSNCleO00SpCmDNGWKGpNHJyZT3O09r5XRco3mjqVH4LjwJhZ6F65ArVch6yp5+QtC8dnUWoVCZFyP9CKGCjZziNTqLYSehyRjdOXXdeB6SA/RebgpRby0tFA4202jurJYsEOGG9twB2OUV1rUJ49jcPLem5ZsQWm7FAch5RmOJkhajZnhzzT7IRmb8Ex7hvbDMAyQ+UyXFuoQJBGsKCIJI6KDXQMKI6QwAi9LGG124E1MNM6cROTv8EdvRNsiiWO4E3OugOSg48u5rpwoHBiscNBrjLc65PCXBXCUlpp7hpvXijgLyeZlEYzFI00ZyJoKY8oX5yBM+/MIsgSjWSfWUfbZ8zxIXhJhD8YIfQ+KYSAOA9iDCUpLTajVMtkUuHSj1U/Rlrt7/hIGV7bBcSyq2c03HeZxEHLlaOgGKC02EFoOrPEEvChAUDLPblFA5IeonlhC6IdwR2P4tovSMvmvz4PRasKZWDA73YylFcKdmIdWPU9DLhuQhiPy5GEYhI6XCb+k4jPoUxFlfPawYFgWtdOrcPpDCq7I3B/d0WRPEZdKOiZbHTJly0gN1zsj2o18WB/YNEROARiNOkoiIMhCodLO7Zl3i43mYdpzZXB5E3qzDqmkw+r0AUODUi7RgyJrnfGiCL1Rg6gpNFh1PTIYa5LEfrI5LphweTg1LwrgeK64P/fDTSniB61SPNPCeLMDlmMRBgHaT59HdXkRnCTM0KWSOCZ+peUgCkIkYVQYWnGiAKs3LIp4mqawepQAEgchoiAkm9osJIKc2SgJKHRc9C6uQ9a1wgCpXF0AJqMjf07fJj8YWaepPwUr8CQEkkQqoPvwR4+CJEkwuLKFOAhoBdknqubVQqXt/hBWb0C5n2MTQXaTHmXuEPk+fNMqwnUTSaTMwHLphswv2CxkOHB9KlxJskfUchAKfx5ZQuD6GG60UT2xBKvTgzshRV0SRdAXGlCqBrxLEwScA6SAXC5Bq5YQ+UHBFFAqJSzdeRajjW3wkgijXoHZ6WGy3UNldZEe2IccxueRceWFBqJqCFbgsf21c5h0etDr1HJRKqXMI6iFyCM/DZZhyQlvSnG885pkIte69Qz9f5Jg0u5BzgRoR4FWLQNpisl2B3EUoXzH2SIMGUC2e+7CHVtgGCr6pYVG1sNJpvjZ+4d2qOUSkKZwhmMwLIfaiaUjUYoPC1nXsPKi2xE4a9RWUWQ4586hurJINF/PB8uTc+lRzlPo+VmOJ1/w/3sXriCNYqjVEhiRozQrSUJlqQmj1Zx5/dCjAp6zlaZdKFmOI0uFTn+WS78LN4edsr61L0vE7o8KVVTz7EkqCLXyHiK+2e3DM+kJb3UHGK5v4dTfeVFRTOZRciI/wGijDYZl4Jk2RFmEbBjkacCykEsavIlFMvnBCN7YhGxodKFtbR7pM9ITeqtgoThjE0lMNxjDZsd2HU5zM+/lejNxVrEQwe4ODizi+YAsH4IJkMiTORsW0kMtKcJ39wPpUWbDMI7Sg70aGJYFL0mIwoi46wIPuWQgDoJC5rwf9tgwKBS4Gzgu3IlVnC96yA9QO7EMSVXhTyxwogBRV1FZItqaPZpAMTQYCw1wPEc6Ap6HUi0jjmhBoZQNGK3GoR/KLEcZoUmSwGz34FsOKllQtD+x0Ti7Vohp8jzO/LUD19sjXso/y/TXlScfHThd3Af5Sns/QQ8FJ5iF0547HBNXfaGOwZUtpNkDhOFYjLa2YaQLECSxsG3N3+Mo4SkHodhdBSE4Sdxz/VO7aXYnIMgyGqfXihbaUR90bNbTTtMU7WfOY7zVoaSl0QSjjS0YCw1yOQwC8Iq85/XTJJ25f3a7UPKiiFqmGL806GAerpp2f6Px6KOPfiPf7hjHOMYx/n+DeWn33/AifoxjHOMYx7hxuLEqkWMc4xjHOMY3FMdF/BjHOMYxnsc4LuJHxLlz5/DSl74U/jXSDm8mTNPEO97xDrz1rW/Ffffdh8cee+xmH9KhkSQJ3vve9+K+++7Dj/7oj+LSpf0zOZ+rCMMQ7373u/GWt7wFb3rTm/Bf/st/udmHdE3o9/v49m//dpw7d+5mH8o148Mf/jDuu+8+/MAP/AAeeuihm30414Wbb4rxPIJlWXj/+98P8Qb6uXwj8Tu/8zv45m/+Ztx///04f/48fv7nfx6///u/f7MP61D40z/9UwRBgE9/+tP48pe/jF/+5V/Ghz70oZt9WEfCH/7hH6JSqeBXf/VXMRqN8MY3vhHf+Z3febMP60gIwxDvfe97IV+DQ+ZzBV/60pfw2GOP4VOf+hRc18VHP/rRm31I14XjlfghkaYp/tk/+2d417veBUW5sWKEbxTuv/9+vPnNbwYAxHEM6evAx/164dFHH8WrXvUqAMDdd9+Nxx9//CYf0dHxute9Dj/7sz8LgK4n7hvoTXOj8P73vx9vfvObsbAwPy3o+YAvfOELuO222/DOd74T73jHO/DqV7/6Zh/SdeF4JT4HDz30ED7+8Y/P/Gx5eRlveMMbcMcdd9ykozoa5n2GBx54AC9+8YvR7Xbx7ne/G7/wC79wk47u6LAsC7q+w/vlOA5RFIF/DjgsHhaaRnx1y7LwMz/zM/i5n/u5m3tAR8TnPvc51Go1vOpVr8Jv/dZv3ezDuWYMh0Nsbm7iwQcfxPr6On7yJ38SDz/88A031/tG4flzB3wDce+99+Lee++d+dl3fdd34bOf/Sw++9nPotvt4u1vfzs++clP3qQjvDrmfQYAeOqpp/Cud70L//gf/2O8/OUvvwlHdm3QdR22bRf/nyTJ86qA59ja2sI73/lOvOUtb8H3fd/33ezDORI++9nPgmEYfPGLX8STTz6J97znPfjQhz6EZvPwVrfPBVQqFZw5cwaiKOLMmTOQJAmDwQD1ev1mH9q1IT3GkfGa17wm9TzvZh/GkfHMM8+k3/M935M++eSTN/tQjoyHH344fc973pOmaZo+9thj6Y//+I/f5CM6Orrdbvq6170ufeSRR272oVw33vrWt6bPPvvszT6Ma8Kf/dmfpffff3+aJEm6vb2dvva1r02jKLrZh3XNeP4tZY5xzfjABz6AIAjwS7/0SwBodft8GQ5+13d9F/77f//vePOb34w0TfHAAw/c7EM6Mh588EFMJhN88IMfxAc/+EEAwEc+8pHn9ZDw+YjXvOY1+Mu//Eu86U1vQpqmeO973/u8nE/kOFZsHuMYxzjG8xjH7JRjHOMYx3ge47iIH+MYxzjG8xjHRfwYxzjGMZ7HOC7ixzjGMY7xPMZxET/GMY5xjOcxjov4MY5xjGM8j3FcxI9xjGMc43mM4yJ+jGMc4xjPY/x/Z9uh5NM0CewAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu')\n",
    "lim = plt.axis()\n",
    "plt.scatter(Xnew[:, 0], Xnew[:, 1], c=ynew, s=20, cmap='RdBu', alpha=0.1)\n",
    "plt.axis(lim);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We see a slightly curved boundary in the classifications—in general, the boundary produced by a Gaussian naive Bayes model will be quadratic.\n",
    "\n",
    "A nice aspect of this Bayesian formalism is that it naturally allows for probabilistic classification, which we can compute using the `predict_proba` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.89, 0.11],\n",
       "       [1.  , 0.  ],\n",
       "       [1.  , 0.  ],\n",
       "       [1.  , 0.  ],\n",
       "       [1.  , 0.  ],\n",
       "       [1.  , 0.  ],\n",
       "       [0.  , 1.  ],\n",
       "       [0.15, 0.85]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yprob = model.predict_proba(Xnew)\n",
    "yprob[-8:].round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The columns give the posterior probabilities of the first and second labels, respectively.\n",
    "If you are looking for estimates of uncertainty in your classification, Bayesian approaches like this can be a good place to start.\n",
    "\n",
    "Of course, the final classification will only be as good as the model assumptions that lead to it, which is why Gaussian naive Bayes often does not produce very good results.\n",
    "Still, in many cases—especially as the number of features becomes large—this assumption is not detrimental enough to prevent Gaussian naive Bayes from being a reliable method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Multinomial Naive Bayes\n",
    "\n",
    "The Gaussian assumption just described is by no means the only simple assumption that could be used to specify the generative distribution for each label.\n",
    "Another useful example is multinomial naive Bayes, where the features are assumed to be generated from a simple multinomial distribution.\n",
    "The multinomial distribution describes the probability of observing counts among a number of categories, and thus multinomial naive Bayes is most appropriate for features that represent counts or count rates.\n",
    "\n",
    "The idea is precisely the same as before, except that instead of modeling the data distribution with the best-fit Gaussian, we model it with a best-fit multinomial distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Example: Classifying Text\n",
    "\n",
    "One place where multinomial naive Bayes is often used is in text classification, where the features are related to word counts or frequencies within the documents to be classified.\n",
    "We discussed the extraction of such features from text in [Feature Engineering](05.04-Feature-Engineering.ipynb); here we will use the sparse word count features from the 20 Newsgroups corpus made available through Scikit-Learn to show how we might classify these short documents into categories.\n",
    "\n",
    "Let's download the data and take a look at the target names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alt.atheism',\n",
       " 'comp.graphics',\n",
       " 'comp.os.ms-windows.misc',\n",
       " 'comp.sys.ibm.pc.hardware',\n",
       " 'comp.sys.mac.hardware',\n",
       " 'comp.windows.x',\n",
       " 'misc.forsale',\n",
       " 'rec.autos',\n",
       " 'rec.motorcycles',\n",
       " 'rec.sport.baseball',\n",
       " 'rec.sport.hockey',\n",
       " 'sci.crypt',\n",
       " 'sci.electronics',\n",
       " 'sci.med',\n",
       " 'sci.space',\n",
       " 'soc.religion.christian',\n",
       " 'talk.politics.guns',\n",
       " 'talk.politics.mideast',\n",
       " 'talk.politics.misc',\n",
       " 'talk.religion.misc']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_20newsgroups\n",
    "\n",
    "data = fetch_20newsgroups()\n",
    "data.target_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "For simplicity here, we will select just a few of these categories and download the training and testing sets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "deletable": true,
    "editable": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "categories = ['talk.religion.misc', 'soc.religion.christian',\n",
    "              'sci.space', 'comp.graphics']\n",
    "train = fetch_20newsgroups(subset='train', categories=categories)\n",
    "test = fetch_20newsgroups(subset='test', categories=categories)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Here is a representative entry from the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subject: Federal Hearing\n",
      "Originator: dmcgee@uluhe\n",
      "Organization: School of Ocean and Earth Science and Technology\n",
      "Distribution: usa\n",
      "Lines: 10\n",
      "\n",
      "\n",
      "Fact or rumor....?  Madalyn Murray O'Hare an atheist who eliminated the\n",
      "use of the bible reading and prayer in public schools 15 years ago is now\n",
      "going to appear before the FCC with a petition to stop the reading of the\n",
      "Gospel on the airways of America.  And she is also campaigning to remove\n",
      "Christmas programs, songs, etc from the public schools.  If it is true\n",
      "then mail to Federal Communications Commission 1919 H Street Washington DC\n",
      "20054 expressing your opposition to her request.  Reference Petition number\n",
      "\n",
      "2493.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(train.data[5][48:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "In order to use this data for machine learning, we need to be able to convert the content of each string into a vector of numbers.\n",
    "For this we will use the TF-IDF vectorizer (introduced in [Feature Engineering](05.04-Feature-Engineering.ipynb)), and create a pipeline that attaches it to a multinomial naive Bayes classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "deletable": true,
    "editable": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "model = make_pipeline(TfidfVectorizer(), MultinomialNB())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "With this pipeline, we can apply the model to the training data and predict labels for the test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "model.fit(train.data, train.target)\n",
    "labels = model.predict(test.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now that we have predicted the labels for the test data, we can evaluate them to learn about the performance of the estimator.\n",
    "For example, let's take a look at the confusion matrix between the true and predicted labels for the test data (see the following figure):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "mat = confusion_matrix(test.target, labels)\n",
    "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
    "            xticklabels=train.target_names, yticklabels=train.target_names,\n",
    "            cmap='Blues')\n",
    "plt.xlabel('true label')\n",
    "plt.ylabel('predicted label');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Evidently, even this very simple classifier can successfully separate space discussions from computer discussions, but it gets confused between discussions about religion and discussions about Christianity.\n",
    "This is perhaps to be expected!\n",
    "\n",
    "The cool thing here is that we now have the tools to determine the category for *any* string, using the `predict` method of this pipeline.\n",
    "Here's a utility function that will return the prediction for a single string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "def predict_category(s, train=train, model=model):\n",
    "    pred = model.predict([s])\n",
    "    return train.target_names[pred[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's try it out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sci.space'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_category('sending a payload to the ISS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'soc.religion.christian'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_category('discussing the existence of God')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'comp.graphics'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_category('determining the screen resolution')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Remember that this is nothing more sophisticated than a simple probability model for the (weighted) frequency of each word in the string; nevertheless, the result is striking.\n",
    "Even a very naive algorithm, when used carefully and trained on a large set of high-dimensional data, can be surprisingly effective."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## When to Use Naive Bayes\n",
    "\n",
    "Because naive Bayes classifiers make such stringent assumptions about data, they will generally not perform as well as more complicated models.\n",
    "That said, they have several advantages:\n",
    "\n",
    "- They are fast for both training and prediction.\n",
    "- They provide straightforward probabilistic prediction.\n",
    "- They are often easily interpretable.\n",
    "- They have few (if any) tunable parameters.\n",
    "\n",
    "These advantages mean a naive Bayes classifier is often a good choice as an initial baseline classification.\n",
    "If it performs suitably, then congratulations: you have a very fast, very interpretable classifier for your problem.\n",
    "If it does not perform well, then you can begin exploring more sophisticated models, with some baseline knowledge of how well they should perform.\n",
    "\n",
    "Naive Bayes classifiers tend to perform especially well in the following situations:\n",
    "\n",
    "- When the naive assumptions actually match the data (very rare in practice)\n",
    "- For very well-separated categories, when model complexity is less important\n",
    "- For very high-dimensional data, when model complexity is less important\n",
    "\n",
    "The last two points seem distinct, but they actually are related: as the dimensionality of a dataset grows, it is much less likely for any two points to be found close together (after all, they must be close in *every single dimension* to be close overall).\n",
    "This means that clusters in high dimensions tend to be more separated, on average, than clusters in low dimensions, assuming the new dimensions actually add information.\n",
    "For this reason, simplistic classifiers like the ones discussed here tend to work as well or better than more complicated classifiers as the dimensionality grows: once you have enough data, even a simple model can be very powerful."
   ]
  }
 ],
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